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Kernel Grouped Multivariate Discriminant Analysis for Hyperspectral Image Classification Read chapter Read first chapter

2014 | OriginalPaper | Chapter

A New Sparse Representation Algorithm for Semi-supervised Signal Classification

Authors : Azam Andalib, Seyed Morteza Babamir

Published in: Artificial Intelligence and Signal Processing

Publisher: Springer International Publishing

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Abstract

The performance of many Sparse Representation (SR) based signal classification tasks is highly dependent on the availability of the datasets with a large amount of labeled data points. However, in many cases, accessing to sufficient labeled data may be expensive or time consuming, whereas acquiring a large amount of unlabeled data is relatively easy. In this paper, we propose a new SR based classification method which utilizes the information of the unlabeled data as well as the labeled data. Experimental results show that the proposed method outperforms the state of the art SR based classification methods.

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Metadata
Title
A New Sparse Representation Algorithm for Semi-supervised Signal Classification
Authors
Azam Andalib
Seyed Morteza Babamir
Copyright Year
2014
Book
Artificial Intelligence and Signal Processing

Print ISBN: 978-3-319-10848-3

Electronic ISBN: 978-3-319-10849-0

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-10849-0_16

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