# Deep Orthogonal Representations: Fundamental Properties and Applications

@article{Hsu2018DeepOR, title={Deep Orthogonal Representations: Fundamental Properties and Applications}, author={Hsiang Hsu and Salman Salamatian and Fl{\'a}vio du Pin Calmon}, journal={ArXiv}, year={2018}, volume={abs/1806.08449} }

Several representation learning and, more broadly, dimensionality reduction techniques seek to produce representations of the data that are orthogonal (uncorrelated). Examples include PCA, CCA, Kernel/Deep CCA, the ACE algorithm and correspondence analysis (CA). For a fixed data distribution, all finite variance representations belong to the same function space regardless of how they are derived. In this work, we present a theoretical framework for analyzing this function space, and demonstrate… CONTINUE READING

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