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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
2012 IEEE Geoscience and Remote Sensing Letters  
Morphological profiles have been proposed in recent literature, as aiding tools to achieve better results for classification of remotely sensed data.  ...  The results show that the NLPCA permits to obtain better classification accuracies than using linear PCA.  ...  CLASSIFICATION RESULTS FOR THE CHRIS DATASET USING SVM CLASSIFICATION ALGORITHM. CLASSIFICATION RESULTS FOR THE CHRIS DATASET USING A NEURAL NETWORK CLASSIFICATION ALGORITHM. ).  ... 
doi:10.1109/lgrs.2011.2172185 fatcat:dciavd5axrdplcxssnukdv6nmq

Kernel Principal Component Analysis for Feature Reduction in Hyperspectrale Images Analysis

Mathieu Fauvel, Jocelyn Chanussot, Jon Benediktsson
2006 Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006  
In this paper, KPCA is used has a preprocessing step to extract relevant feature for classification and to prevent from the Hughes phenomenon.  ...  By mapping the data onto another feature space and using nonlinear function, Kernel PCA (KPCA) can extract higher order statistics.  ...  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.  ... 
doi:10.1109/norsig.2006.275232 fatcat:yke3vgpfwfcetpxtofcj6rdbfy

Principal manifolds and Bayesian subspaces for visual recognition

B. Moghaddam
1999 Proceedings of the Seventh IEEE International Conference on Computer Vision  
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition.  ...  Abstract We i n vestigate the use of linear and nonlinear principal manifolds for learning lowdimensional representations for visual recognition.  ...  Nonlinear Principal Manifolds One of the simplest methods for computing nonlinear principal manifolds is the nonlinear PCA (NLPCA) autoassociative multi-layer neural network [16, 8] shown in Figure  ... 
doi:10.1109/iccv.1999.790407 dblp:conf/iccv/Moghaddam99 fatcat:uew4mkxlvbeh7bdtjwu4fxg2b4

The new method of Extraction and Analysis of Non-linear Features for face recognition

Ali Mahdavi Hormat, Karim Faez, Zeynab Shokoohi, Mohammad Zaher Karimi
2012 International Journal of Electrical and Computer Engineering (IJECE)  
In this paper, we introduce the new method of Extraction and Analysis of Non-linear Features (EANF) for face recognition based on extraction and analysis of nonlinear features i.e.  ...  In our proposed algorithm, EANF removes disadvantages such as the length of search space, different sizes and qualities of imagees due to various conditions of imaging time that has led to problems in  ...  point of h-NLPCA network with the simple solution of PCA.  ... 
doi:10.11591/ijece.v2i6.1773 fatcat:i32xmprbzvejjj2xoasswuufdi

Nonlinear data description with Principal Polynomial Analysis

V. Laparra, D. Tuia, S. Jimenez, G. Camps-Valls, J. Malo
2012 2012 IEEE International Workshop on Machine Learning for Signal Processing  
Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Per formance of PCA is however hampered when data exhibits nonlinear feature relations.  ...  Successful performance of the proposed PPA is illustrated in dimension ality reduction, in compact representation of non-Gaussian image textures, and multispectral image classification.  ...  For those data manifolds lacking the re quired symmetry, nonlinear modifications of PCA would be more appropriate: the residual nonlinear dependence after PCA should be removed.  ... 
doi:10.1109/mlsp.2012.6349786 dblp:conf/mlsp/LaparraTJCM12 fatcat:yqdrznghqjf4hhcq2bu37nok6u

Markerless gating for lung cancer radiotherapy based on machine learning techniques

Tong Lin, Ruijiang Li, Xiaoli Tang, Jennifer G Dy, Steve B Jiang
2009 Physics in Medicine and Biology  
ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods.  ...  (artificial neural networks-ANN and SVM).  ...  For classification, in addition to SVM, we combine the dimensionality reduction techniques with a three-layer artificial neural network (ANN) for gated lung cancer radiotherapy.  ... 
doi:10.1088/0031-9155/54/6/010 pmid:19229098 fatcat:ix7y3tmvwfaadjnxplr7tzaeve

Predicting and grouping digitized paintings by style using unsupervised feature learning

Eren Gultepe, Thomas Edward. Conturo, Masoud Makrehchi
2018 Journal of Cultural Heritage  
Classification accuracy and F-score were similar/higher compared to other classification methods using more complex feature learning models (e.g., convolutional neural networks, a supervised algorithm)  ...  For feature-based classification of paintings, the macro-averaged F-score was 0.469.  ...  PCA is not a component of our nonlinear feature extraction approach, but we performed PCA for comparison. We performed PCA on the image dataset by using singular value decomposition.  ... 
doi:10.1016/j.culher.2017.11.008 pmid:30034259 pmcid:PMC6051702 fatcat:m6g5bkxjsvel7mbnryvsfgf3ky

Classification of reduction invariants with improved backpropagation

S. M. Shamsuddin, M. Darus, M. N. Sulaiman
2002 International Journal of Mathematics and Mathematical Sciences  
This paper proposes an improved error signal of backpropagation network for classification of the reduction invariants using principal component analysis, for extracting the bulk of the useful information  ...  Higher order centralised scale- invariants are used to extract features of handwritten digits before PCA, and the reduction invariants are sent to the improved backpropagation model for classification  ...  and image classification since 1960s.  ... 
doi:10.1155/s0161171202006117 fatcat:heivlyl43vbpnnrau5upuwaa3u

A Nonlinear Principal Component Analysis of Image Data

R. SAEGUSA
2005 IEICE transactions on information and systems  
In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of the principal components.  ...  In this paper, we reduce the dimensionality of image data using the proposed method, and examine its effectiveness in the compression and recognition of images.  ...  and "Waseda University grant for special research projects, No. 2004B-882."  ... 
doi:10.1093/ietisy/e88-d.10.2242 fatcat:4ah5m5qnkvgnthuywnlta77tt4

An application of KPCA and SVM in the human face recognition

Feng Yue, Meng Qing Song, Yuan Hai Bo
2013 International Journal of Security and Its Applications  
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.  ...  However, only the second-order statistics of the image is considered in the PCA method, which cannot be extended for the data of the high order statistics when the nonlinear correlation between pixels  ... 
doi:10.14257/ijsia.2013.7.6.30 fatcat:dlnrqctl5bcibk6hdyh2r2eehi

CMVF

Jialie Shen, Anne H. H. Ngu, John Shepherd, Du Q. Huynh, Quan Z. Sheng
2003 Proceedings of the 2003 ACM SIGMOD international conference on on Management of data - SIGMOD '03  
for nonlinear correlations can be handled by NLDR.  ...  The linear PCA appears at the bottom, the nonlinear neural network is at the top, and the representation of lower dimensional vectors appears in the hidden layer.  ... 
doi:10.1145/872757.872842 dblp:conf/sigmod/ShenNSHS03 fatcat:vba4n5y4mbgbrnuju5tvjewzma

CMVF

Jialie Shen, Anne H. H. Ngu, John Shepherd, Du Q. Huynh, Quan Z. Sheng
2003 Proceedings of the 2003 ACM SIGMOD international conference on on Management of data - SIGMOD '03  
for nonlinear correlations can be handled by NLDR.  ...  The linear PCA appears at the bottom, the nonlinear neural network is at the top, and the representation of lower dimensional vectors appears in the hidden layer.  ... 
doi:10.1145/872841.872842 fatcat:mbj6f47iijgvna2zekgug6c2ee

Computational Intelligence-Based Biometric Technologies

D. Zhang, Wangmeng Zuo
2007 IEEE Computational Intelligence Magazine  
CI-based methods, including neural network and fuzzy technologies, have also been extensively investigated for biometric matching.  ...  Varieties of evolutionary computation and neural networks techniques have been successfully applied to biometric data representation and dimensionality reduction.  ...  Moghaddam and Yang carried out a comprehensive evaluation of classification methods for recognizing gender from facial images. The best classification performance was that of SVM [87] .  ... 
doi:10.1109/mci.2007.353418 fatcat:aynahy3ttbesfl3qm3u25gcawq

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
2003 Manipulation and Analysis of Biomolecules, Cells, and Tissues  
We have previously shown that neural network classifiers using sets of numerical features computed from fluorescence microscope images were able to recognize all major subcellular location patterns with  ...  Current classifiers are limited by under-determined classification boundaries due to the limited number of available images compared to the number of features.  ...  for extracting nonlinear principal components.  ... 
doi:10.1117/12.477903 fatcat:qovbslehijeajbfnedbrhpdlse

Incremental Nonlinear PCA for Classification [chapter]

Byung Joo Kim, Il Kon Kim
2004 Lecture Notes in Computer Science  
The purpose of this study is to propose a new online and nonlinear PCA(OL-NPCA) method for feature extraction from the incremental data.  ...  Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the following problems.  ...  Proposed Classification System In earlier Section 3 we proposed an incremental nonlinear PCA method for nonlinear feature extraction.  ... 
doi:10.1007/978-3-540-30116-5_28 fatcat:uhxxflm54ngntdr2v66lnnu5dy
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