• Mathematics, Computer Science
• Published in AAAI 2018
• DOI:10.1609/aaai.v33i01.33015281

An Efficient Approach to Informative Feature Extraction from Multimodal Data

@article{Wang2018AnEA,
  title={An Efficient Approach to Informative Feature Extraction from Multimodal Data},
  author={Lichen Wang and Jiaxiang Wu and Shao-Lun Huang and Lizhong Zheng and Xiangxiang Xu and Lin Zhang and Junzhou Huang},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.08979}
}

• Lichen Wang, Jiaxiang Wu, +4 authors Junzhou Huang

One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-R\'{e}nyi (HGR) maximal correlation becomes an appealing objective because of its operational meaning and desirable properties. However, the strict whitening constraints formalized in the HGR maximal correlation limit its application. To address this problem, this paper proposes Soft-HGR, a novel… CONTINUE READING

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