We propose a method for sparse and robust principal component analysis.
The methodology is structured in two steps: first, a robust estimate of the covariance
matrix is obtained, then this estimate is plugged-in into an elastic-net regression which
enforces sparseness. Our approach provides an intuitive, general and flexible extension
of sparse principal component analysis to the robust setting. We also show how to
implement the algorithm when the dimensionality exceeds the number of observations
by adapting the approach to the use of robust loadings from ROBPCA. The proposed
technique is seen to compare well for simulated and real datasets.

EB00000004104K

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• A plug-in approach to sparse and robust principal component analysis