Multimodal sparse Bayesian dictionary learning applied to multimodal data classification

Igor Fedorov; Bhaskar D. Rao; Truong Q. Nguyen

ICASSP 2017accepted0 citations

Multimodal sparse Bayesian dictionary learning applied to multimodal data classification

Igor Fedorov, Bhaskar D. Rao, Truong Q. Nguyen

Abstract

In this paper, we present a novel multimodal sparse dictionary learning algorithm based on a hierarchical sparse Bayesian framework. The framework allows for enforcing joint sparsity across dictionaries without restricting the actual entries to be equal. We show that the proposed method is able to learn dictionaries of higher quality than existing approaches. We validate our claims with extensive experiments on synthetic data as well as real-world data.

BibTeX

@inproceedings{icassp2017_multimodalsparse,
  title = {Multimodal sparse Bayesian dictionary learning applied to multimodal data classification},
  author = {Igor Fedorov and Bhaskar D. Rao and Truong Q. Nguyen},
  booktitle = {ICASSP 2017},
  year = {2017}
}