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Non-negative local coordinate factorization for image representation

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3 Author(s)
Yan Chen ; The State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China 310058 ; Hujun Bao ; Xiaofei He

Recently Non-negative Matrix Factorization (NMF) has become increasingly popular for feature extraction in computer vision and pattern recognition. NMF seeks for two non-negative matrices whose product can best approximate the original matrix. The non-negativity constraints lead to sparse, parts-based representations which can be more robust than non-sparse, global features. To obtain more accurate control over the sparseness, in this paper, we propose a novel method called Non-negative Local Coordinate Factorization (NLCF) for feature extraction. NLCF adds a local coordinate constraint into the standard NMF objective function. Specifically, we require that the learned basis vectors be as close to the original data points as possible. In this way, each data point can be represented by a linear combination of only few nearby basis vectors, which naturally leads to sparse representation. Extensive experimental results suggest that the proposed approach provides a better representation and achieves higher accuracy in image clustering.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on

Date of Conference:

20-25 June 2011