Conference proceeding
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), Vol.29, pp.766-774
Advances in Neural Information Processing Systems
01/01/2016
Abstract
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.
Details
- Title: Subtitle
- Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
- Creators
- Weiran Wang - Toyota Technol Inst, Chicago, IL 60637 USAJialei Wang - University of ChicagoDan Garber - Toyota Technol Inst, Chicago, IL 60637 USANathan Srebro - Toyota Technol Inst, Chicago, IL 60637 USA
- Contributors
- D D Lee (Editor)M Sugiyama (Editor)U V Luxburg (Editor)I Guyon (Editor)R Garnett (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), Vol.29, pp.766-774
- Publisher
- Neural Information Processing Systems (Nips)
- Series
- Advances in Neural Information Processing Systems
- ISSN
- 1049-5258
- Number of pages
- 9
- Grant note
- 1546500 / NSF BIGDATA
- Language
- English
- Date published
- 01/01/2016
- Academic Unit
- Computer Science
- Record Identifier
- 9984696575502771
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