An implementation of Deep Canonical Correlation Analysis (DCCA or Deep CCA) in Python with plain library like numpy and scipy need installed.
DCCA is a method to learn com-plex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated.
DCCA is originally proposed in the paper: Andrew G, Arora R, Bilmes J, et al. Deep canonical correlation analysis[C]//International Conference on Machine Learning. 2013: 1247-1255.
Many followers use this idea for many fields like following paper for matching images and text:
Yan F, Mikolajczyk K. Deep correlation for matching images and text[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3441-3450.
Wang W, Arora R, Livescu K, et al. On deep multi-view representation learning[C]//International Conference on Machine Learning. 2015: 1083-1092.
Zhang X, Jin C, Zhang Y, et al. Image Tag Recommendation via Deep Cross-Modal Correlation Mining[M]//Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer, Cham, 2016: 437-449.