Latent common manifold learning with alternating diffusion: Analysis and applications

Published

Journal Article

© 2018 Elsevier Inc. 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.

Full Text

Duke Authors

Cited Authors

  • Talmon, R; Wu, HT

Published Date

  • January 1, 2018

Published In

Electronic International Standard Serial Number (EISSN)

  • 1096-603X

International Standard Serial Number (ISSN)

  • 1063-5203

Digital Object Identifier (DOI)

  • 10.1016/j.acha.2017.12.006

Citation Source

  • Scopus