NDLI: Learning Semantic Patterns with Discriminant Localized Binary Projections

Content Provider	IEEE Xplore Digital Library
Author	Shuicheng Yan Tianqiang Yuan Xiaoou Tang
Copyright Year	2006
Description	Author affiliation: Chinese University of Hong Kong, Hong Kong (Shuicheng Yan)
Abstract	In this paper, we present a novel approach to learning semantic localized patterns with binary projections in a supervised manner. The pursuit of these binary projections is reformulated into a problem of feature clustering, which optimizes the separability of different classes by taking the members within each cluster as the nonzero entries of a projection vector. An efficient greedy procedure is proposed to incrementally combine the sub-clusters by ensuring the cardinality constraints of the projections and the increase of the objective function. Compared with other algorithms for sparse representations, our proposed algorithm, referred to as Discriminant Localized Binary Projections (dlb), has the following characteristics: 1) dlb is supervised, hence is much more effective than other unsupervised sparse algorithms like Non-negative Matrix Factorization (NMF) in terms of classification power; 2) similar to NMF, dlb can derive spatially localized sparse bases; furthermore, the sparsity of dlb is controllable, and an interesting result is that the bases have explicit semantics in human perception, like eyes and mouth; and 3) classification with dlb is extremely efficient, and only addition operations are required for dimensionality reduction. Extensive experimental results show significant improvements of dlb in sparsity and face recognition accuracy in comparison to the state-of-the-art algorithms for dimensionality reduction and sparse representations.
Starting Page	168
Ending Page	174
File Size	442560
Page Count	7
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.173
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Sparse matrices Principal component analysis Linear discriminant analysis Tensile stress Image reconstruction Asia Humans Eyes Mouth
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition 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

Discriminant analysis with tensor representation

Element Rearrangement for Tensor-Based Subspace Learning

Sparse Subspace Denoising for Image Manifolds

Fast Pixel/Part Selection with Sparse Eigenvectors

Rank-one Projections with Adaptive Margins for Face Recognition

Non-negative graph embedding

STPCA: Sparse tensor Principal Component Analysis for feature extraction

Sparse projections for high-dimensional binary codes

Face Recognition using Discriminatively Trained Orthogonal Rank One Tensor Projections

Learning Semantic Patterns with Discriminant Localized Binary Projections

Similar Documents

Discriminant analysis with tensor representation

Element Rearrangement for Tensor-Based Subspace Learning

Sparse Subspace Denoising for Image Manifolds

Fast Pixel/Part Selection with Sparse Eigenvectors

Rank-one Projections with Adaptive Margins for Face Recognition

Non-negative graph embedding

STPCA: Sparse tensor Principal Component Analysis for feature extraction

Sparse projections for high-dimensional binary codes

Face Recognition using Discriminatively Trained Orthogonal Rank One Tensor Projections

Learning Semantic Patterns with Discriminant Localized Binary Projections