Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20900
Title: A linear subspace learning approach via sparse coding
Authors: Zhang, L 
Zhu, P
Hu, Q
Zhang, D 
Issue Date: 2011
Source: Proceedings of the IEEE International Conference on Computer Vision, 2011, p. 755-761 How to cite?
Abstract: Linear subspace learning (LSL) is a popular approach to image recognition and it aims to reveal the essential features of high dimensional data, e.g., facial images, in a lower dimensional space by linear projection. Most LSL methods compute directly the statistics of original training samples to learn the subspace. However, these methods do not effectively exploit the different contributions of different image components to image recognition. We propose a novel LSL approach by sparse coding and feature grouping. A dictionary is learned from the training dataset, and it is used to sparsely decompose the training samples. The decomposed image components are grouped into a more discriminative part (MDP) and a less discriminative part (LDP). An unsupervised criterion and a supervised criterion are then proposed to learn the desired subspace, where the MDP is preserved and the LDP is suppressed simultaneously. The experimental results on benchmark face image databases validated that the proposed methods outperform many state-of-the-art LSL schemes.
Description: 2011 IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, 6-13 November 2011
URI: http://hdl.handle.net/10397/20900
ISBN: 9781457711015
DOI: 10.1109/ICCV.2011.6126313
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

22
Last Week
1
Last month
1
Citations as of Sep 11, 2017

WEB OF SCIENCETM
Citations

10
Last Week
0
Last month
0
Citations as of Sep 13, 2017

Page view(s)

40
Last Week
1
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.