An iterative coordinate descent algorithm to compute sparse low-rank approximations

In this paper, we describe a new algorithm to build few sparse principal components from given data matrix. Our approach does not explicitly create the covariance matrix of and can be viewed as an extension Kogbetliantz approximate singular value decomposition for components. We show performance proposed recover on various datasets literature perform dimensionality reduction classification applications.