NDLI: Sparse linear representations for recognition

Content Provider	IEEE Xplore Digital Library
Author	Lei Cheng Xiuwen Liu
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci., Florida State Univ., Tallahassee, FL, USA (Lei Cheng; Xiuwen Liu)
Abstract	Recently, it has been argued that sparse coding is an important principle for recognition, which has been used effectively to derive filters with desirable properties. However there is no effective algorithm to link the sparse coding principle to the recognition performance. Our experiments show that commonly used sparse bases often give worse recognition performance compared to other linear bases. In this paper, we propose a criterion consisting of weighted combination of recognition performance and sparseness. Using a Monte Carlo simulated annealing algorithm, we obtain linear bases with sparse representation as well as good recognition performance. We also find an interesting relationship among commonly used linear representations by comparing their sparseness and recognition performance.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	1324
Ending Page	1327
File Size	277531
Page Count	4
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223887
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Independent component analysis Computer science Filters Simulated annealing Image analysis Computational efficiency Image coding Image reconstruction Visual system
Content Type	Text
Resource Type	Article

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