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

2016 | OriginalPaper | Chapter

Transductive Classification by Robust Linear Neighborhood Propagation

Authors : Lei Jia, Zhao Zhang, Weiming Jiang

Published in: Advances in Multimedia Information Processing - PCM 2016

Publisher: Springer International Publishing

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Abstract

We propose an enhanced label prediction method termed Transductive Classification Robust Linear Neighborhood Propagation (R-LNP). To encode the neighborhood reconstruction error more accurately, we apply the L2,1-norm that is proved to be very robust to noise for characterizing the manifold smoothing term. Since L2,1-norm can also enforce the neighborhood reconstruction error to be sparse in rows, i.e., entries of some rows are zeros. In addition, to enhance robustness in the process of modeling the difference between the initial labels and predicted ones, we also regularize the weighted L2,1-norm on the label fitting term, so the resulted measures would be more accurate. Compared with several transductive label propagation models, our proposed algorithm obtains state-of-the-art performance over extensive representation and classification experiments.

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previous chapter Fine-Grained Vehicle Recognition in Traffic Surveillance
next chapter Discriminative Sparse Coding by Nuclear Norm-Driven Semi-Supervised Dictionary Learning
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Metadata
Title
Transductive Classification by Robust Linear Neighborhood Propagation
Authors
Lei Jia
Zhao Zhang
Weiming Jiang
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_29