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The Ethereal
The Fast Convergence of Incremental PCA
January 15, 2015 ยท Declared Dead ยท ๐ Neural Information Processing Systems
"No code URL or promise found in abstract"
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Authors
Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
arXiv ID
1501.03796
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
148
Venue
Neural Information Processing Systems
Last Checked
3 months ago
Abstract
We consider a situation in which we see samples in $\mathbb{R}^d$ drawn i.i.d. from some distribution with mean zero and unknown covariance A. We wish to compute the top eigenvector of A in an incremental fashion - with an algorithm that maintains an estimate of the top eigenvector in O(d) space, and incrementally adjusts the estimate with each new data point that arrives. Two classical such schemes are due to Krasulina (1969) and Oja (1983). We give finite-sample convergence rates for both.
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