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2015 | OriginalPaper | Chapter

Discriminative Orthonormal Dictionary Learning for Fast Low-Rank Representation

Authors : Zhen Dong, Mingtao Pei, Yunde Jia

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

This paper presents a discriminative orthonormal dictionary learning method for low-rank representation. The orthonormal property is beneficial for the representative power of the dictionary by avoiding the dictionary redundancy. To enhance the discriminative power of the dictionary, all the class-specific dictionaries which are encouraged to well represent the samples from the same class are optimized simultaneously. With the learned discriminative orthonormal dictionary, the low-rank representation problem can be solved much faster than traditional methods. Experiments on three public datasets demonstrate the effectiveness and efficiency of our method.

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Metadata
Title
Discriminative Orthonormal Dictionary Learning for Fast Low-Rank Representation
Authors
Zhen Dong
Mingtao Pei
Yunde Jia
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26531-5

Electronic ISBN: 978-3-319-26532-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26532-2_10

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