  The analysis of data sets arising from multiple sensors has drawn significant
research attention over the years. Traditional methods, including kernel-based
methods, are typically incapable of capturing nonlinear geometric structures.
We introduce a latent common manifold model underlying multiple sensor
observations for the purpose of multimodal data fusion. A method based on
alternating diffusion is presented and analyzed; we provide theoretical
analysis of the method under the latent common manifold model. To exemplify the
power of the proposed framework, experimental results in several applications
are reported.
