NDLI: Sparse data-dependent kernel principal component analysis based on least squares support vector machine for feature extraction and recognition

Content Provider	Springer Nature Link
Author	Li, Jun Bao Gao, Huijun
Copyright Year	2011
Abstract	Kernel learning is widely used in many areas, and many methods are developed. As a famous kernel learning method, kernel principal component analysis (KPCA) endures two problems in the practical applications. One is that all training samples need to be stored for the computing the kernel matrix during kernel learning. Second is that the kernel and its parameter have the heavy influence on the performance of kernel learning. In order to solve the above problem, we present a novel kernel learning namely sparse data-dependent kernel principal component analysis through reducing the training samples with sparse learning-based least squares support vector machine and adaptive self-optimizing kernel structure according to the input training samples. Experimental results on UCI datasets, ORL and YALE face databases, and Wisconsin Breast Cancer database show that it is feasible to improve KPCA on saving consuming space and optimizing kernel structure.
Starting Page	1971
Ending Page	1980
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	8
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-05-03
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Kernel method Kernel principal component analysis Sparse learning Data-dependent kernel function Feature extraction Computation efficiency Computational Biology/Bioinformatics Computational Science and Engineering Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Kernel based intrusion detection system

An efficient KPCA algorithm based on feature correlation evaluation

An efficient Kernel-based matrixized least squares support vector machine

Sparse margin–based discriminant analysis for feature extraction

Feature extraction using maximum variance sparse mapping

Improvement of the kernel minimum squared error model for fast feature extraction

Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares

An improved SOM algorithm and its application to color feature extraction

Local sparse representation projections for face recognition

Sparse data-dependent kernel principal component analysis based on least squares support vector machine for feature extraction and recognition

Similar Documents

Kernel based intrusion detection system

An efficient KPCA algorithm based on feature correlation evaluation

An efficient Kernel-based matrixized least squares support vector machine

Sparse margin–based discriminant analysis for feature extraction

Feature extraction using maximum variance sparse mapping

Improvement of the kernel minimum squared error model for fast feature extraction

Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares

An improved SOM algorithm and its application to color feature extraction

Local sparse representation projections for face recognition

Sparse data-dependent kernel principal component analysis based on least squares support vector machine for feature extraction and recognition