Generalized Power Method for Sparse Principal Component Analysis

@article{Journe2010GeneralizedPM,
  title={Generalized Power Method for Sparse Principal Component Analysis},
  author={Michel Journ{\'e}e and Yurii Nesterov and Peter Richt{\'a}rik and Rodolphe Sepulchre},
  journal={Journal of Machine Learning Research},
  year={2010},
  volume={11},
  pages={517-553}
}
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively. While the initial formulations involve nonconvex functions, and are therefore computationally intractable, we rewrite them into the form of an optimization program involving maximization… CONTINUE READING
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