An Efficient Approach to Informative Feature Extraction from Multimodal Data


  • Lichen Wang Tsinghua University
  • Jiaxiang Wu Tencent AI Lab
  • Shao-Lun Huang TBSI
  • Lizhong Zheng Massachusetts Institute of Technology
  • Xiangxiang Xu Tsinghua University
  • Lin Zhang Tsinghua University
  • Junzhou Huang University of Texas at Arlington



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-Rényi (HGR) maximal correlation be-´ comes 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 framework to extract informative features from multiple data modalities. Specifically, our framework prevents the “hard” whitening constraints, while simultaneously preserving the same feature geometry as in the HGR maximal correlation. The objective of Soft-HGR is straightforward, only involving two inner products, which guarantees the efficiency and stability in optimization. We further generalize the framework to handle more than two modalities and missing modalities. When labels are partially available, we enhance the discriminative power of the feature representations by making a semi-supervised adaptation. Empirical evaluation implies that our approach learns more informative feature mappings and is more efficient to optimize.




How to Cite

Wang, L., Wu, J., Huang, S.-L., Zheng, L., Xu, X., Zhang, L., & Huang, J. (2019). An Efficient Approach to Informative Feature Extraction from Multimodal Data. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5281-5288.



AAAI Technical Track: Machine Learning