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Poster
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
Weiran Wang · Jialei Wang · Dan Garber · Dan Garber · Nati Srebro

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #116
in Wednesday Posters »

We study the stochastic optimization of canonical correlation analysis (CCA), whose objective is nonconvex and does not decouple over training samples. Although several stochastic gradient based optimization algorithms have been recently proposed to solve this problem, no global convergence guarantee was provided by any of them. Inspired by the alternating least squares/power iterations formulation of CCA, and the shift-and-invert preconditioning method for PCA, we propose two globally convergent meta-algorithms for CCA, both of which transform the original problem into sequences of least squares problems that need only be solved approximately. We instantiate the meta-algorithms with state-of-the-art SGD methods and obtain time complexities that significantly improve upon that of previous work. Experimental results demonstrate their superior performance.

Keywords: Large Scale Learning and Big Data · Convex Optimization · Component Analysis (ICA,PCA,CCA, FLDA)
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Author Information

Weiran Wang (University of California, Merced)
Jialei Wang (University of Chicago)
Dan Garber (Technion)
Dan Garber (Toyota Technological Institute at Chicago)

Dan's research interests lie in the intersection of machine learning and continuous optimization. Dan's main focus is on the development of efficient algorithms with novel and provable performance guarantees for basic machine learning, data analysis, decision making and optimization problems. Dan received both his Ph.D and his M.Sc degrees from the Technion - Israel Institute of Technology, where he worked under the supervision of Prof. Elad Hazan. Before that, Dan completed his bachelor's degree in computer engineering, also in the Technion.

Nati Srebro (TTI-Chicago)

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