Learning a factor model via regularized PCA

Authors:
Yi-Hao Kao;Benjamin Roy
Affiliations:
Stanford University, Stanford, USA;Stanford University, Stanford, USA
Venue:
Machine Learning
Year:
2013

Citing 7
Cited 0

Bayesian PCA

Proceedings of the 1998 conference on Advances in neural information processing systems II
Robust factor analysis

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Eigenvalues of large sample covariance matrices of spiked population models

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Robust principal component analysis?

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Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

Foundations and Trends® in Machine Learning

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Abstract

We consider the problem of learning a linear factor model. We propose a regularized form of principal component analysis (PCA) and demonstrate through experiments with synthetic and real data the superiority of resulting estimates to those produced by pre-existing factor analysis approaches. We also establish theoretical results that explain how our algorithm corrects the biases induced by conventional approaches. An important feature of our algorithm is that its computational requirements are similar to those of PCA, which enjoys wide use in large part due to its efficiency.