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
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