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Published in:
2010 | OriginalPaper | Chapter
m-SNE: Multiview Stochastic Neighbor Embedding
Authors : Bo Xie, Yang Mu, Dacheng Tao
Published in: Neural Information Processing. Theory and Algorithms
Publisher: Springer Berlin Heidelberg
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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
$O\left(1/k^2\right)$
, 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.