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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2018

07.02.2017 | Theoretical Advances

Matrix-based subspace analysis with the general norm for image feature extraction

verfasst von: Zhizheng Liang

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2018

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Abstract

Classical matrix-based subspace analysis tries to exploit the structured information (rows or columns) of images for extracting image features. However, it is not robust in dealing with contaminated data. In this paper, novel models for matrix-based subspace analysis with robust objective functions and general norms are developed to address this problem. In the proposed models, general vector norms are employed to control the sparsity of projection vectors and the alternative optimization technique is utilized to solve the proposed models due to the characteristics of the models. Nonnegative constraints are also imposed on projection vectors to encourage their sparsity in the proposed models. A deflation scheme for image data is devised to achieve arbitrary pairs of projection vectors. A series of experiments on some face data sets are conducted to demonstrate that the proposed models are more effective than previous methods in handling contaminated data.
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Metadaten
Titel
Matrix-based subspace analysis with the general norm for image feature extraction
verfasst von
Zhizheng Liang
Publikationsdatum
07.02.2017
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2018
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-017-0603-1

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