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Poster
Stochastic Approximation for Canonical Correlation Analysis
Raman Arora · Teodor Vanislavov Marinov · Poorya Mianjy · Nati Srebro

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #52
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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 $\epsilon$-suboptimality in the population objective in $\operatorname{poly}(\frac{1}{\epsilon})$ iterations. We also consider practical variants of the proposed algorithms and compare them with other methods for CCA both theoretically and empirically.
Keywords: Components Analysis (e.g., CCA, ICA, LDA, PCA)
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Author Information

Raman Arora (Johns Hopkins University)
Teodor Vanislavov Marinov (Johns Hopkins University)
Poorya Mianjy (Johns Hopkins University)
Nati Srebro (TTI-Chicago)

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