m-SNE: Multiview stochastic neighbor embedding
- Publication Type:
- Conference Proceeding
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6443 LNCS (PART 1), pp. 338 - 346
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
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. © 2010 Springer-Verlag.
Please use this identifier to cite or link to this item: