Abstract
In this paper, we address the problem of hidden common variables discovery from multimodal data sets of nonlinear high-dimensional observations. We present a metric based on local applications of canonical correlation analysis (CCA) and incorporate it in a kernel-based manifold learning technique. We show that this metric discovers the hidden common variables underlying the multimodal observations by estimating the Euclidean distance between them. Our approach can be viewed both as an extension of CCA to a nonlinear setting as well as an extension of manifold learning to multiple data sets. Experimental results show that our method indeed discovers the common variables underlying high-dimensional nonlinear observations without assuming prior rigid model assumptions.
Original language | English |
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Article number | 7742895 |
Pages (from-to) | 1101-1115 |
Number of pages | 15 |
Journal | IEEE Transactions on Signal Processing |
Volume | 65 |
Issue number | 5 |
DOIs | |
State | Published - 1 Mar 2017 |
Keywords
- CCA
- Diffusion maps
- Metric learning
- Multimodal
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering