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Kernel principal component analysis network for image classification [article]

Dan Wu, Jiasong Wu, Rui Zeng, Longyu Jiang, Lotfi Senhadji, Huazhong Shu
<span title="2015-12-20">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed  ...  First, mapping the data into higher space with kernel principal component analysis to make the data linearly separable. Then building a two-layer KPCANet to obtain the principal components of image.  ...  [3] constructed a principal co mponent analysis network (PCANet), which cascaded principal co mponent analysis (PCA), binary hashing, and block-wise histogram.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1512.06337v1">arXiv:1512.06337v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qnwldiyk3fe37hb7rpxoyiqxim">fatcat:qnwldiyk3fe37hb7rpxoyiqxim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200901021616/https://arxiv.org/ftp/arxiv/papers/1512/1512.06337.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9d/55/9d55ec73cab779403cd933e6eb557fb04892b634.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1512.06337v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Kernel Principal Component Analysis for Feature Reduction in Hyperspectrale Images Analysis

Mathieu Fauvel, Jocelyn Chanussot, Jon Benediktsson
<span title="">2006</span> <i title="IEEE"> Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006 </i> &nbsp;
Principal component analysis (PCA) has shown to be a good unsupervised feature extraction. On the other hand, this methods only focus on second orders statistics.  ...  In this paper, KPCA is used has a preprocessing step to extract relevant feature for classification and to prevent from the Hughes phenomenon.  ...  The authors would like to thank the IAPR -TC7 for provinding the data and Prof. Paolo Gamba and Prof. Fabio Dell'Acqua of the University of Pavia, Italy, for providing reference data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/norsig.2006.275232">doi:10.1109/norsig.2006.275232</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yke3vgpfwfcetpxtofcj6rdbfy">fatcat:yke3vgpfwfcetpxtofcj6rdbfy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170811105239/http://mistis.inrialpes.fr/people/fauvel/Site/Publication_files/norsig_fauvel_chanussot_bendiktsson.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f8/86/f8860fa786819b1d8b3c472f094fc7919fc696a7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/norsig.2006.275232"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Hyperspectral image classification based on spectral-spatial kernel principal component analysis network

Yanguo Fan, Shizhe Hou, Dingfeng Yu, W. Qin, L. Wang, V. Yepes
<span title="">2020</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wehsdgmkgvabfewguumql7pepe" style="color: black;">E3S Web of Conferences</a> </i> &nbsp;
In this paper, a Spatial-Spectral Kernel Principal Component Analysis Network (SS-KPCANet) was proposed. The network is developed from the original structure of Principal Component Analysis Network.  ...  How to combine spatial information with spectral information effectively has always been a research hotspot of hyperspectral image classification.  ...  In this paper, we proposed a Spatial-Spectral Kernel Principal Component Analysis Network (SS-KPCANet) for hyperspectral image classification inspired by PCANet.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/e3sconf/202016503001">doi:10.1051/e3sconf/202016503001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vuiusi3z6nf4hhnaattwd2njfq">fatcat:vuiusi3z6nf4hhnaattwd2njfq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200505195829/https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/25/e3sconf_caes2020_03001.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d2/47/d24781ba9af2101a1da40b0b5bc70c17263398ca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/e3sconf/202016503001"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

A robust classifier combined with an auto-associative network for completing partly occluded images

Takashi Takahashi, Takio Kurita
<span title="">2005</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oml24fsyizfuhn3rn5np75ubdi" style="color: black;">Neural Networks</a> </i> &nbsp;
This paper describes an approach for constructing a classifier which is unaffected by occlusions in images. We propose a method for integrating an auto-associative network into a simple classifier.  ...  It is shown that the classification performance is not decreased, even if about 30% of the face image is occluded. q Neural Networks 18 (2005) 958-966 www.elsevier.com/locate/neunet 0893-6080/$ -see front  ...  Acknowledgements This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in Aid for Young Scientists (B), No. 15700204. Appendix. Multinomial logit model  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neunet.2005.03.011">doi:10.1016/j.neunet.2005.03.011</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/15936926">pmid:15936926</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jebyvkxm4vhs5lon66vzfy2tse">fatcat:jebyvkxm4vhs5lon66vzfy2tse</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808213610/http://www.css.risk.tsukuba.ac.jp/kashin/papers/2-1/NN2005-Takahashi.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bb/8b/bb8b554a759f9a1a914416b73ae62c5ee176eaac.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neunet.2005.03.011"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

Yong Fan, Yong Liu, Tianzi Jiang, Zhening Liu, Yihui Hao, Haihong Liu, Benoit M. Dawant, David R. Haynor
<span title="2010-03-04">2010</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ha25cznnjncxtjoykhsg6fz5ly" style="color: black;">Medical Imaging 2010: Image Processing</a> </i> &nbsp;
The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases  ...  In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components.  ...  The features used in this face image classification study are equivalent to the component specific time courses computed in fMRI image analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.844495">doi:10.1117/12.844495</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/miip/FanLJLHL10.html">dblp:conf/miip/FanLJLHL10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ap44ngynk5d6bpa34czpq4znfe">fatcat:ap44ngynk5d6bpa34czpq4znfe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809224544/http://www.nlpr.ia.ac.cn/2010papers/gjhy/gh110.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f4/13/f413b5f0be643c34397c1bd3d416a22213278b73.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.844495"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

An application of KPCA and SVM in the human face recognition

Feng Yue, Meng Qing Song, Yuan Hai Bo
<span title="2013-11-30">2013</span> <i title="Science and Engineering Research Support Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4h7flaxsjnh6he3qbiwvxbekxe" style="color: black;">International Journal of Security and Its Applications</a> </i> &nbsp;
Face Recognition is more and more researchers' attention, especially the principal Component Analysis method (Principle Component Analysis, PCA) after the application of Face Recognition, Face Recognition  ...  Predictably, the two aspects research results in the human visual and non rigid body of will be helpful to find the end solution for face feature extraction and description.  ...  Figure 3 . 3 Overall Process First, the kernel principal component analysis is used to extract the face images, and then the classification and recognition is done through the kernel principal component  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14257/ijsia.2013.7.6.30">doi:10.14257/ijsia.2013.7.6.30</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dlnrqctl5bcibk6hdyh2r2eehi">fatcat:dlnrqctl5bcibk6hdyh2r2eehi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180602113933/http://www.sersc.org/journals/IJSIA/vol7_no6_2013/31.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/33/76/3376656bddb1ad5df54f9eaec066b8bc9833746d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14257/ijsia.2013.7.6.30"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images

Kai Huang, Meel Velliste, Robert F. Murphy, Dan V. Nicolau, Joerg Enderlein, Robert C. Leif, Daniel L. Farkas
<span title="2003-06-19">2003</span> <i title="SPIE"> Manipulation and Analysis of Biomolecules, Cells, and Tissues </i> &nbsp;
Specifically, principal component analysis, kernel principal component analysis, nonlinear principal component analysis, independent component analysis, classification trees, fractal dimensionality reduction  ...  The best results were obtained using stepwise discriminant analysis and we found that as few as eight features can provide good classification accuracy for all major subcellular patterns in HeLa cells.  ...  In this paper, four different methods were applied, including principal component analysis (PCA), nonlinear principal component analysis (NLPCA), kernel principal component analysis (KPCA), and independent  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.477903">doi:10.1117/12.477903</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qovbslehijeajbfnedbrhpdlse">fatcat:qovbslehijeajbfnedbrhpdlse</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20040206011717/http://murphylab.web.cmu.edu:80/publications/90-huang2003.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/48/f2/48f292baf0bc72c16ca94f52802efc04eab69f23.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.477903"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Detection of endogenous foreign bodies in Chinese hickory nuts by hyperspectral spectral imaging at the pixel level

Zhe Feng, 1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China, Weihao Li, Di Cui, 2. Key Laboratory of On Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
<span title="">2022</span> <i title="International Journal of Agricultural and Biological Engineering (IJABE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ty5euaovuzgqvkwcbof3fchdvm" style="color: black;">International Journal of Agricultural and Biological Engineering</a> </i> &nbsp;
Two classical classification methods, principal component analysis-K-nearest neighbors (PCA-KNN) and the support vector machine (SVM), were employed to establish identification models for comparison.  ...  In this study, a deep learning approach based on a two-dimensional convolutional neural network (2D CNN) and long short-term memory (LSTM) integrated with hyperspectral imaging for distinguishing the shells  ...  The authors gratefully acknowledge the financial support of the National Key Research and Development Program of China (Grant No. 2017YFC1600805) and the help of Jie Yang in studying convolution neural networks  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.25165/j.ijabe.20221502.6881">doi:10.25165/j.ijabe.20221502.6881</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fm3ryh7skzderkg6k7h2pbehwi">fatcat:fm3ryh7skzderkg6k7h2pbehwi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220505220156/https://www.ijabe.org/index.php/ijabe/article/download/6881/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9b/83/9b830ad3d5e6886448263086e494659121878734.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.25165/j.ijabe.20221502.6881"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles

Giorgio Licciardi, Prashanth Reddy Marpu, Jocelyn Chanussot, Jon Atli Benediktsson
<span title="">2012</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2cxanfrsvclzbz73u2mnqrxru" style="color: black;">IEEE Geoscience and Remote Sensing Letters</a> </i> &nbsp;
Morphological profiles are in general built using features containing most of the information content of the data, such as the components derived from principal component analysis (PCA).  ...  Morphological profiles have been proposed in recent literature, as aiding tools to achieve better results for classification of remotely sensed data.  ...  A comparison with the classification accuracies obtained using standard PCA and kernel PCA with the EMP, shows the enhancement introduced by the nonlinear principal component analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/lgrs.2011.2172185">doi:10.1109/lgrs.2011.2172185</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dciavd5axrdplcxssnukdv6nmq">fatcat:dciavd5axrdplcxssnukdv6nmq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190504065444/https://hal.archives-ouvertes.fr/hal-00797814/document" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a0/c5/a0c5d38b5e2b181e1571d56463c61b21e3bc0104.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/lgrs.2011.2172185"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Discriminant analysis of functional connectivity patterns on Grassmann manifold

Yong Fan, Yong Liu, Hong Wu, Yihui Hao, Haihong Liu, Zhening Liu, Tianzi Jiang
<span title="">2011</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders  ...  Rather than analyzing each network encoded by a spatial independent component separately, we propose a novel algorithm for discriminant analysis of functional brain networks jointly at an individual level  ...  The features used in this face image classification study are equivalent to the component specific time courses computed in fMRI image analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2011.03.051">doi:10.1016/j.neuroimage.2011.03.051</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21440643">pmid:21440643</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/57uvktrkbjapfeca5umqzrtqe4">fatcat:57uvktrkbjapfeca5umqzrtqe4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808234938/http://nlpr-web.ia.ac.cn/2011papers/gjkw/gk46.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ac/5f/ac5f909316950435947c1ff1fb1b9818ce958ff2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2011.03.051"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

A Convolutional Neural Network Based Approach for SAR Image Classification of Vehicles

Abhishek Ameta, R.V.C.E
<span title="2020-06-19">2020</span> <i title="ESRSA Publications Pvt. Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3j6n6lpsjndinobmibtprywohe" style="color: black;">International Journal of Engineering Research and</a> </i> &nbsp;
This paper describes a Convolutional Neural Network based approach for SAR image classification.  ...  In this paper the 1-D feature are extracted from using principle component analysis. The 1-D feature vector set is given as input to the CNN layer.  ...  The feature extraction is performed using Principal Component Analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17577/ijertv9is060250">doi:10.17577/ijertv9is060250</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hgt5q7n5ffh45d4u4zq4h2ry44">fatcat:hgt5q7n5ffh45d4u4zq4h2ry44</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200709102212/https://www.ijert.org/research/a-convolutional-neural-network-based-approach-for-sar-image-classification-of-vehicles-IJERTV9IS060250.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/27/44/2744c826aa7e72a8c0a0b7885de31f24341fe110.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17577/ijertv9is060250"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

REAL-TIME CLASSIFICATION OF FACIAL EXPRESSIONS USING A PRINCIPAL COMPONENT ANALYSIS AND CONVOLUTIONAL NEURAL NETWORK

Dwi Lydia Zuharah Astuti, Samsuryadi Samsuryadi, Dian Palupi Rini
<span title="2019-10-09">2019</span> <i title="Universitas Mercu Buana"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/huipx5lrgbhf7gg7tantj7kq5y" style="color: black;">Jurnal Ilmiah SINERGI</a> </i> &nbsp;
This study combined both methods in the classification of facial expressions, namely the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods.  ...  Classification of facial expressions has become an essential part of computer systems and human-computer fast interaction.  ...  The paper proposes a Principal Component Analysis (PCA) method and a Convolutional Neural Network (CNN) method to classify facial expressions in multi-view and unrestricted environments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22441/sinergi.2019.3.008">doi:10.22441/sinergi.2019.3.008</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dlkjgefr3veoxnantfpl6dnzfe">fatcat:dlkjgefr3veoxnantfpl6dnzfe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200709131517/https://sinergi.mercubuana.ac.id/media/290503-real-time-classification-of-facial-expre-2c76b344.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a8/38/a83887df63887445dee5127c026ef3a0352ac802.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22441/sinergi.2019.3.008"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Identification of rice seed varieties using neural network

Zhao-yan Liu, Fang Cheng, Yi-bin Ying, Xiu-qin Rao
<span title="">2005</span> <i title="Zhejiang University Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xybt5y45ijaejndxa2udmt7b64" style="color: black;">JZUS-A - Journal of Zhejiang University. Science</a> </i> &nbsp;
Two hundred and forty kernels used as the training data set and sixty kernels as the test data set in the neural network used to identify rice seed varieties.  ...  Seven color and fourteen morphological features were used for discriminant analysis.  ...  ACKNOWLEDGEMENT We would like to express our special thanks to the Zhejiang Province Seeds Company and the Hybrid Rice Institute of China for supplying samples in the research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1631/jzus.2005.b1095">doi:10.1631/jzus.2005.b1095</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/16252344">pmid:16252344</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC1390657/">pmcid:PMC1390657</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zyskzbisszcoxg2r4amg3fmcw4">fatcat:zyskzbisszcoxg2r4amg3fmcw4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200206044636/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC1390657&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0f/50/0f50c6b8abce49f4f65228728ddea1d2339c42e4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1631/jzus.2005.b1095"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1390657" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Face Recognition using Two Dimension Fractional Discrete Cosine Domain and BPNN

Kumud Arora, V.P. Vishwakarma, Poonam Garg
<span title="2015-03-18">2015</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
Experimental result shows marked reduction in classification error rate with neural network classification. 46 with accuracy from the training images even though some sort of distortion or deformation  ...  Comparison is conducted for fractional order feature classification accuracy of AT&T public database with nearest neighbour classification approach.  ...  reduction approach of Principal component analysis (PCA) [4] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/19865-1842">doi:10.5120/19865-1842</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w7y733ob4naonjrx6bpd5tozqq">fatcat:w7y733ob4naonjrx6bpd5tozqq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812140525/http://research.ijcaonline.org/volume113/number10/pxc3901842.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/64/18/64182bde056f39170ae1eb83af93d88f191f774a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/19865-1842"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Functional Brain Network Classification With Compact Representation of SICE Matrices

Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li
<span title="">2015</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nrcoa2vuhjcvfctty6zgus57um" style="color: black;">IEEE Transactions on Biomedical Engineering</a> </i> &nbsp;
Therefore, we propose to employ manifold-based similarity measures and kernel-based PCA to extract principal connectivity components as a compact representation of brain network.  ...  Therefore, we propose to employ manifold-based similarity measures and kernel-based PCA to extract principal connectivity components as a compact representation of brain network.  ...  Principal component analysis (PCA), the commonly used unsupervised dimensionality reduction method, is a natural option for this task.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tbme.2015.2399495">doi:10.1109/tbme.2015.2399495</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25667346">pmid:25667346</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ozrvt6hr3jew3jd6dab62ochte">fatcat:ozrvt6hr3jew3jd6dab62ochte</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180719204843/http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5193&amp;context=eispapers" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/69/ff/69ff9528eaba3aa13251a100f5336fb8c407ac2d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tbme.2015.2399495"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>
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