[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2018.09.011

Semi-supervised Multi-view Manifold Discriminant Intact Space Learning

Han, Lu (College of Automation, Nanjing University of Posts and Telecommunications)
Wu, Fei (College of Automation, Nanjing University of Posts and Telecommunications)
Jing, Xiao-Yuan (College of Automation, Nanjing University of Posts and Telecommunications)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.9, 2018 , pp. 4317-4335 More about this Journal

Abstract

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ( $SM^2DIS$ ) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

Keywords

Semi-supervised learning; Manifold discriminant intact space; Generated multi-view data points; Intact feature representations; Image classification;

Citations & Related Records

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