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 \(O\left(1/k^2\right)\), 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.
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Xie, B., Mu, Y., Tao, D. (2010). m-SNE: Multiview Stochastic Neighbor Embedding. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_42
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DOI: https://doi.org/10.1007/978-3-642-17537-4_42
Publisher Name: Springer, Berlin, Heidelberg
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