NDLI: Orthogonal sparsity preserving projections for feature extraction

Content Provider	IEEE Xplore Digital Library
Author	Chao Lan Xiao-Yuan Jing Qian Liu Shi-Qiang Gao Yong-Fang Yao
Copyright Year	2010
Description	Author affiliation: Colleage of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China (Chao Lan; Xiao-Yuan Jing; Qian Liu; Shi-Qiang Gao; Yong-Fang Yao)
Abstract	Sparse representation has been extensively studied in the signal processing community, which surprisingly pointed out that one target signal can be accurately represented as a linear combination of very few measurement signals, often called atoms, in a given dictionary. This discovery has soon been employed to the field of pattern recognition and more recently, given rise to a newly developed unsupervised feature extraction method named sparsity preserving projections (SPP), which aims at seeking a linear embedded space where the sparse reconstructive relations among the data in the dictionary can be preserved. However, SPP is non-orthogonal and still has some space for improvement. Specially, by taking into consideration the preservation of some neat property of a dictionary, this paper presents an orthogonal sparsity preserving projections (OSPP). OSPP iteratively calculate a projective vector which can preserve the sparse reconstructive relations as SPP dose, and at the same enforcing it to be orthogonal to all previously obtained vectors. Empirical study shows that OSPP has more powerful sparsity preserving ability than SPP and hence is expected to have better classification performance, since sparsity is potentially related to discrimination. Experiments on the public Yale face databases validate the effectiveness of OSPP, as compared with several representative unsupervised feature extraction methods.
Starting Page	176
Ending Page	179
File Size	138325
Page Count	4
File Format	PDF
ISBN	9781424465309
e-ISBN	9781424465316
DOI	10.1109/ICWAPR.2010.5576322
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Databases Face recognition Sparse Representation Feature Extraction Feature extraction Unsupervised Learning Face Orthogonal Projective Vectors Principal component analysis
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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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