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Title: Sparse representation based spectral regression
Authors: Yu, G
Yu, Z
Hua, J
Li, X
You, J 
Keywords: Discriminative
Neighborhood graph
Sparse representation
Spectral regression
Issue Date: 2011
Publisher: IEEE
Source: 2011 International Conference on Machine Learning and Cybernetics (ICMLC), 10-13 July 2011, Guilin, p. 532-537 How to cite?
Abstract: Spectral regression is a newly proposed method for dimensionality reduction, which is also based on graph embedding but less time and space consuming. However, like many methods based on neighborhood graphs, it still focuses on local manifold smoothness and ignores discriminative information among samples. In this paper, instead of making use of a neighborhood graph, we take advantage of a global graph defined by the coefficients of sparse representation and propose a method called Sparse Representation based Spectral Regression (SpSR) on this graph. This graph is data-driven, discriminative and robust to noise features. Experimental results on facial images feature extraction tasks demonstrate these advantages.
ISBN: 978-1-4577-0305-8
ISSN: 2160-133X
DOI: 10.1109/ICMLC.2011.6016791
Appears in Collections:Conference Paper

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