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The Ethereal
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
July 31, 2015 ยท Declared Dead ยท ๐ International Conference on Machine Learning
"No code URL or promise found in abstract"
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Authors
Ohad Shamir
arXiv ID
1507.08788
Category
cs.LG: Machine Learning
Cross-listed
math.NA,
math.OC,
stat.ML
Citations
96
Venue
International Conference on Machine Learning
Last Checked
3 months ago
Abstract
We study the convergence properties of the VR-PCA algorithm introduced by \cite{shamir2015stochastic} for fast computation of leading singular vectors. We prove several new results, including a formal analysis of a block version of the algorithm, and convergence from random initialization. We also make a few observations of independent interest, such as how pre-initializing with just a single exact power iteration can significantly improve the runtime of stochastic methods, and what are the convexity and non-convexity properties of the underlying optimization problem.
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