DocumentCode :
2543652
Title :
Global Sparse Representation Projections for Feature Extraction and Classification
Author :
Lai, Zhihui ; Jin, Zhong ; Yang, Jian
Author_Institution :
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2009
fDate :
4-6 Nov. 2009
Firstpage :
1
Lastpage :
5
Abstract :
In this paper, we propose a novel supervised learning method called Global Sparse Representation Projections (GSRP) for linear dimensionality reduction. GSRP can be viewed as a combiner of sparse representation and manifold learning. But differing from the recent manifold learning methods such as Local Preserving Projections (LPP), GSRP introduces the global sparse representation information into the objective function. Since sparse representation can implicitly employ the "local" structure of the data by imposing the sparsity prior, we take advantages of this property to characterize the local structure. By combining the local interclass neighborhood relationship and sparse representation information, GSRP aims to preserve the sparse reconstructive relationship of the data and simultaneously maximize the interclass separability. Comprehensive comparison and extensive experiments show that GSRP achieves higher recognition rates than the state-of-the-art techniques such as LPP and Sparsity Preserving Projections (SPP).
Keywords :
feature extraction; learning (artificial intelligence); feature classification; feature extraction; global sparse representation projections; local preserving projections method; manifold learning; supervised learning method; Analysis of variance; Feature extraction; Information analysis; Kernel; Laplace equations; Learning systems; Linear discriminant analysis; Performance analysis; Testing; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4199-0
Type :
conf
DOI :
10.1109/CCPR.2009.5344136
Filename :
5344136
Link To Document :
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