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Visual Tracking by Local Superpixel Matching with Markov Random Field Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Discriminative Sparse Coding by Nuclear Norm-Driven Semi-Supervised Dictionary Learning

Authors : Weiming Jiang, Zhao Zhang, Yan Zhang, Fanzhang Li

Published in: Advances in Multimedia Information Processing - PCM 2016

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a Nuclear norm-driven Semi-Supervised Dictionary Learning (N-SSDL) approach for classification. N-SSDL incorporates the idea of the recent label consistent KSVD with the label propagation process that propagates label information from labeled data to unlabeled data via balancing the neighborhood reconstruction error and the label fitness error. To provide a more reliable distance metric for measuring the neighborhood reconstruction error, we apply the nuclear-norm that is proved to be suitable for modeling the reconstruction error, where the reconstruction coefficients are computed based on the sparsely reconstructed training data rather than original ones. Besides, we also use the robust l 2,1 -norm regularization on the label fitness error so that the measurement is robust to noise and outliers. Extensive simulations on several datasets show that N-SSDL can deliver enhanced performance over other state-of-the-arts for classification.

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previous chapter Transductive Classification by Robust Linear Neighborhood Propagation
next chapter Semantically Smoothed Refinement for Everyday Concept Indexing
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Metadata
Title
Discriminative Sparse Coding by Nuclear Norm-Driven Semi-Supervised Dictionary Learning
Authors
Weiming Jiang
Zhao Zhang
Yan Zhang
Fanzhang Li
Copyright Year
2016
Book
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48889-9

Electronic ISBN: 978-3-319-48890-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48890-5_30