Local Canonical Correlation Analysis for Nonlinear Common Variables Discovery

Or Yair, Ronen Talmon

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number7742895
Pages (from-to)1101-1115
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume65
Issue number5
DOIs
StatePublished - 1 Mar 2017

Keywords

  • CCA
  • Diffusion maps
  • Metric learning
  • Multimodal

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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