Classification using generalized partial least squares

Advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene function, but they also present the challenge of analyzing data with a large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. We address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in the context of generalized linear regression, based on a previous approach, iteratively reweighted partial least squares, that is, IRWPLS. We compare our results with two-stage PLS and with other classifiers. We show that by phrasing the problem in a generalized linear model setting and by applying Firth's procedure to avoid (quasi)separation, we often get lower classification error rates.