Semi-supervised sparse coding

Jim Jing-Yan Wang, Xin Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.
Original languageEnglish (US)
Title of host publication2014 International Joint Conference on Neural Networks (IJCNN)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1630-1637
Number of pages8
ISBN (Print)9781479914845
DOIs
StatePublished - Sep 10 2014

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