Learning a Factor Model via Regularized PCA

Details Learning a Factor Model via Regularized PCA Learning a Factor Model via Regularized PCA

2011-11-26

arxiv.org/abs/1111.6201v2

Computer Science

Learning

Scientific paper

We consider the problem of learning a linear factor model with an unknown number of factors. 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 elucidate the manner in which our algorithm corrects biases induced by conventional PCA. An important feature of our algorithm is its computational efficiency, which is close to that of PCA, which enjoys wide use in large part due to its efficiency.

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