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