m-SNE: multiview stochastic neighbor embedding

Authors:
Bo Xie;Yang Mu;Dacheng Tao
Affiliations:
School of Computer Engineering, Nanyang Technological University;School of Computer Engineering, Nanyang Technological University Mingli Song, College of Computer Science, Zhejiang University;Centre for Quantum computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology, Sydney
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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Abstract

In many real world applications, different features (or multiview data) can be obtained and how to duly utilize them in dimension reduction is a challenge. Simply concatenating them into a long vector is not appropriate because each view has its specific statistical property and physical interpretation. In this paper, we propose a multiview stochastic neighbor embedding (m-SNE) that systematically integrates heterogeneous features into a unified representation for subsequent processing based on a probabilistic framework. Compared with conventional strategies, our approach can automatically learn a combination coefficient for each view adapted to its contribution to the data embedding. Also, our algorithm for learning the combination coefficient converges at a rate of O(1/k2), which is the optimal rate for smooth problems. Experiments on synthetic and real datasets suggest the effectiveness and robustness of m-SNE for data visualization and image retrieval.