Filters








31,516 Hits in 6.3 sec

A fast kernel dimension reduction algorithm with applications to face recognition

An, Liu, Svetha Venkatesh, Tjahyadi
2005 2005 International Conference on Machine Learning and Cybernetics  
2005, A fast kernel dimension reduction algorithm with applications to face recognition, in ICMLC 2005 : Abstract: This paper presents a novel dimensionality reduction algorithm for kernel based classification  ...  2005, A fast kernel dimension reduction algorithm with applications to face recognition, in ICMLC 2005 : This is the published version: An, Senjian, Liu, Wanquan, Venkatesh, Svetha and Tjahyadi, Ronny  ...  Dimension Reduction for Clustered Data Given a training set {(xT y)}L1 with input data xc £ Rm and class labels yW £ (-1, 14, the task of dimension reduction for kernel based classification is to find  ... 
doi:10.1109/icmlc.2005.1527524 fatcat:4f6n33ygyfgtthxuvk6ph7k46a

Study on the Recognition of Electrogastric Signals Combining Multiple Decomposition Features

2016 Revista de la Facultad de Ingeniería  
For the convenience of calculation, appropriate processing is necessary. In this paper, a method of kernel principal component analysis is used due to the characteristics of the waveform data.  ...  After analyzing the classifier of kernel learning, relevance vector machine is used for data learning and classifying after dimension reduction.  ...  KPCA-based Dimension reduction After using EMD and LMD for decomposition, a 24-demensional eigenvector can be obtained by using the features combined by sampling entropy and fractal dimension.  ... 
doi:10.21311/002.31.6.17 fatcat:ceio3evbwvc77n4g6xjfkctbfm

Fast Classification in Incrementally Growing Spaces [chapter]

Oscar Déniz-Suárez, Modesto Castrillón, Javier Lorenzo, Gloria Bueno, Mario Hernández
2011 Lecture Notes in Computer Science  
In this paper a novel approach is described for fast classification in a PCA+SVM scenario.  ...  Easy samples will thus be classified using less features, thus producing a faster decision.  ...  For the dimensionality reduction approach the results will depend on the number n of dimensions used (i.e. the size of the feature space).  ... 
doi:10.1007/978-3-642-21257-4_38 fatcat:dgok7e4a5jaixbcikzp3c7eulu

Palm Vein Recognition Based on NPE and KELM

Saisai Sun, Xiaoyan Cong, Ping Zhang, Bo Sun, Xiumei Guo
2021 IEEE Access  
This paper proposes a palm vein recognition algorithm based on Neighborhood Preserving Embedding (NPE) and Kernel Extreme Learning Machine (KELM).  ...  The algorithm firstly performs gray-scale normalization processing on vein images, then extracts neighborhood preserving embedding dimensionality reduction features, and finally uses extreme learning machine  ...  Therefore, the NPE dimensionality reduction algorithm is more suitable for reducing the dimension of palmvein images, and can effectively extract the dimensionality reduction features. C.  ... 
doi:10.1109/access.2021.3079458 fatcat:nsx7hytztvblbgfy5yl3ol6biq

An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data

Hongyu Zhang, Limin Jiang, Jijun Tang, Yijie Ding
2021 Frontiers in Cell and Developmental Biology  
By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion.  ...  Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification  ...  Tsne Feature Visualization Tsne is a non-linear dimensionality reduction method, which can map the high-dimensional feature data to the low-dimensional Performance of Various Classifiers Three dimensionally  ... 
doi:10.3389/fcell.2021.615747 pmid:33763416 pmcid:PMC7982914 fatcat:kqbbeadfkjhebliwcleu5n4wqu

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

Muhammad Faisal Siddiqui, Ahmed Wasif Reza, Jeevan Kanesan, Gajendra P. S. Raghava
2015 PLoS ONE  
Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features.  ...  For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel.  ...  Therefore, the new image will be converted into Á and the dimension decreases. However, still the dimension is large enough; as a result, there is a need of more reduction in dimension.  ... 
doi:10.1371/journal.pone.0135875 pmid:26280918 pmcid:PMC4539225 fatcat:zv2l7slo6jbh5f3wt5fhh6epka

IRIS Recognition based on Non Linear Dimensionality Reduction of IRIS Code with KPCA and SVM based Classification

V. V.SatyanarayanaTallapragada, E. G. Rajan
2012 International Journal of Computer Applications  
features by kernel based classifier is difficult.  ...  Hence in this work we emphasize on extracting the most significant feature set from the segmented IRIS and project the features in a high dimensional space using KPCA dimensionality reduction technique  ...  Therefore a suitable dimensionality reduction technique is well suited to reduce the dimensions and hence the size of the feature vectors and followed by classifying the vectors using kernel based classifier  ... 
doi:10.5120/6326-8681 fatcat:7wjoiem65nbypaacaela4dv5um

A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data

Micheal O. Arowolo, Marion O. Adebiyi, Ayodele A. Adebiyi, Olatunji J. Okesola
2020 IEEE Access  
In this study, a hybrid dimensionality reduction technique proposes an optimized Genetic algorithm to pick pertinent subset features from the data.  ...  Support vector machine kernel classifiers used the reduced malaria vector dataset to assess the classification performance of the experiment.  ...  DIMENSIONALITY REDUCTION Dimensionality reduction is a well-known method for removing noises and redundant features.  ... 
doi:10.1109/access.2020.3029234 fatcat:bcstpuhn5jglpleovje47ounpu

Adaptive Multi-scale Tracking Target Algorithm through Drone

Qiusheng He, Xiuyan Shao, Wei Chen, Xiaoyun Li, Xiao Yang, Tongfeng Sun
2019 IEICE transactions on communications  
reduction for color features is analyzed by principal component analysis.  ...  In order to solve the influence of scale change on target tracking using the drone, a multi-scale target tracking algorithm is proposed which based on the color feature tracking algorithm.  ...  termine the locations for soil samples based on a soil map created from drone imaging [4] . A drone is used to collect fast gas concentration data from underground coal fire [5] .  ... 
doi:10.1587/transcom.2018drp0040 fatcat:pfuqnpfrpbfy3h2yxvl7itboia

A Graph-Embedding Approach to Hierarchical Visual Word Mergence

Lei Wang, Lingqiao Liu, Luping Zhou
2017 IEEE Transactions on Neural Networks and Learning Systems  
Appropriately merging visual words is an effective dimension reduction method for the bag-of-visual-words model in image classification.  ...  In terms of computational efficiency, we show that our algorithm can seamlessly integrate a fast search strategy developed in our previous work, and thus well maintain the state-of-the-art merging speed  ...  This calls for effective dimension reduction approaches.  ... 
doi:10.1109/tnnls.2015.2509062 pmid:26742149 fatcat:6eq35tlph5dltgqm5tisadidwq

An Evaluation Scheme for Safe Biometric Feature Extraction Algorithms

Ashly George, Shameem Kappan, Dr. R. Vijayakumar
2017 International Journal of Engineering Research and  
This paper proposes a methodology for evaluation of algorithms for feature extraction in face recognition process.  ...  The paper also covers a survey on existing methodologies for face recognition algorithms available in literature namely, Principle Component Analysis, Linear Discriminant Analysis, Kernel Principle Component  ...  statistical technique used in image recognition and classification.LDA is well known for feature extraction and dimensionality reduction based on fisher faces LDA project the data onto a lower-dimension  ... 
doi:10.17577/ijertv6is080225 fatcat:ssf6f6irvzf7pmytizde5mp5pq

Multiclass classifiers based on dimension reduction with generalized LDA

Hyunsoo Kim, Barry L. Drake, Haesun Park
2007 Pattern Recognition  
Linear discriminant analysis (LDA) has been widely used for dimension reduction of data sets with multiple classes.  ...  A marginal linear discriminant classifier, a Bayesian linear discriminant classifier, and a onedimensional Bayesian linear discriminant classifier are introduced for multiclass classification.  ...  This material is based upon work supported in part by the National Science Foundation Grants CCR-0204109 and ACI-0305543.  ... 
doi:10.1016/j.patcog.2007.03.002 fatcat:4digsv6axzayhmay3w44m7kgni

FEATURE DIMENSION REDUCTION FOR MICROARRAY DATA ANALYSIS USING LOCALLY LINEAR EMBEDDING

SHI CHAO, CHEN LIHUI
2005 Proceedings of the 3rd Asia-Pacific Bioinformatics Conference  
Due to the ultra high dimensionality nature of microarray data, data dimension reduction has drawn special attention for such type of data analysis.  ...  In this paper, we proposed to use a revised locally linear embedding(LLE) method, which is purely unsupervised and fast as the feature extraction strategy for microarray data analysis.  ...  our feature reduction algorithm.  ... 
doi:10.1142/9781860947322_0021 fatcat:nstexvwmvretlewdlj3mz77en4

Stratifying patients using fast multiple kernel learning framework: case studies of Alzheimer's disease and cancers

Thanh-Trung Giang, Thanh-Phuong Nguyen, Dang-Hung Tran
2020 BMC Medical Informatics and Decision Making  
We optimised the data integration, dimension reduction, and kernel fusion. Our framework has great potential for mining large-scale cohort data and aiding personalised prevention.  ...  In this paper, we propose a fast-multiple kernel learning framework, referred to as fMKL-DR, that optimise equations to calculate matrix chain multiplication and reduce dimensions in data space.  ...  Acknowledgements The results published here are in whole or part based upon data generated by the ADNI (https://adni.loni.usc.edu), and TCGA Research Network (https:// www.cancer.gov/tcga).  ... 
doi:10.1186/s12911-020-01140-y pmid:32546157 pmcid:PMC7296686 fatcat:fs5hiqrrtngoppzstzfpbyh76a

Strip Surface Defects Recognition Based on PSO-RS&SOCP-SVM Algorithm

Dongyan Cui, Kewen Xia
2017 Mathematical Problems in Engineering  
used on the optimal selection of strip surface defect image decision features, which removed redundant attributes, provided reduction data for the follow-up Support Vector Machine (SVM) model, reduced  ...  In order to improve the strip surface defect recognition and classification accuracy and efficiency, Rough Set (RS) attribute reduction algorithm based on Particle Swarm Optimization (PSO) algorithm was  ...  Acknowledgments This work was supported by the National Natural Science Foundation of China (no. 51208168) and Hebei Province Foundation for Returned Scholars (no. C2012003038).  ... 
doi:10.1155/2017/4257273 fatcat:5lnf43ihrjhqzkld7i5g364a3y
« Previous Showing results 1 — 15 out of 31,516 results