Iterative orthonormalized partial least squares with sparsity constraints

Orthonormalized partial least squares (OPLS) is a popular multivariate analysis method to perform supervised feature extraction. In this paper, we propose a novel scheme to solve Orthonormalized Partial Least Squares (OPLS) that can be easily modified to include additional constraints over the input data projection vectors. This scheme is used to implement an OPLSmethod with sparsity constraints (SOPLS), which allows to obtain more interpretable projection vectors that depend only on a few of the original input variables. The discriminative power of the sparse features extracted by SOPLS is analyzed on a benchmark of classification problems, where the method shows very competitive performance in terms of classification error.