Multimodal metric learning with local CCA

In this paper, we address the problem of multimodal signal processing from a kernel-based manifold learning standpoint. We propose a data-driven method for extracting the common hidden variables from two multimodal sets of nonlinear high-dimensional observations. To this end, we present a metric based on local canonical correlation analysis (CCA). 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. We test our method in simulations, where we show that it indeed discovers the common variables hidden in high-dimensional nonlinear observations without assuming prior rigid model assumptions.