NDLI: Exploring the natural discriminative information of sparse representation for feature extraction

Content Provider	IEEE Xplore Digital Library
Author	Chao Lan Xiao-yuan Jing Sheng Li Lu-sha Bian Yong-fang Yao
Copyright Year	2010
Description	Author affiliation: College of Automation, Nanjing University of Posts and Telecommunications (NUPT), Nanjing, China (Chao Lan; Xiao-yuan Jing; Sheng Li; Lu-sha Bian; Yong-fang Yao)
Abstract	Sparse representation has been extensively studied in the signal processing community, which shows that one target sample can be accurately recovered by a sparse linear combination of the overall data. Such discovery has soon been applied to the pattern recognition task and, more recently, given rise to two new feature extraction methods, namely sparsity preserving projections (SPP) and global sparse representation projections (GSRP). However, both methods utilized the sparse representation by simply preserving it in the embedded space, but none of them investigates its natural discriminative information and therefore may have limited classifying power for the recognition task. In this paper, we propose a novel feature extraction method by exploring the discriminative information naturally embodied in the sparse representation. Based on the idea that one target sample shall ideally be more accurately reproduced by the intra-class data associated with the sparse coefficients than by the inter-class data, we seek a linearly transformed space where the reconstructive errors of samples caused by intra-class data are minimized and the reconstructive errors caused by inter-class data are simultaneously maximized. We name the proposed method sparse representation-based discriminative information exploring transform (DIET) and experiments on two face databases, i.e., Yale and ORL validate the effectiveness of DIET, as compared with several representative linear feature extraction methods.
Starting Page	916
Ending Page	920
File Size	338070
Page Count	5
File Format	PDF
ISBN	9781424465132
e-ISBN	9781424465163
DOI	10.1109/CISP.2010.5646901
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Face recognition feature extraction Transforms face recognition Feature extraction linear transform Sparse matrices Face Principal component analysis sparse representation discriminative information
Content Type	Text
Resource Type	Article

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