Latent common manifold learning with alternating diffusion: analysis and applications
Data Analysis, Statistics and Probability
2017-08-04 v2 Data Structures and Algorithms
Numerical Analysis
Machine Learning
Abstract
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.
Cite
@article{arxiv.1602.00078,
title = {Latent common manifold learning with alternating diffusion: analysis and applications},
author = {Ronen Talmon and Hau-tieng Wu},
journal= {arXiv preprint arXiv:1602.00078},
year = {2017}
}