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Stochastic Approximation for Canonical Correlation Analysis

2020-02-12 | |  64 |   35 |   0

Abstract 

We propose novel first-order stochastic approximation algorithms for canonical correlation analysis (CCA). Algorithms presented are instances of inexact matrix stochastic gradient (MSG) and inexact matrix exponentiated gradient (MEG), and achieve image.png-suboptimality in the population objective in poly( image.png ) iterations. We also consider practical variants of the proposed algorithms and compare them with other methods for CCA both theoretically and empirically.

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