Powered partial least squares discriminant analysis
From the fundamental parts of PLS-DA, Fisher's canonical discriminant analysis (FCDA) and Powered PLS (PPLS), we develop the concept of powered PILS for classification problems (PPLS-DA). By taking advantage of a sequence of data reducing linear transformations (consistent with the computation of ordinary PLS-DA components), PPLS-DA computes each component from the transformed data by maximization of a parameterized Rayleigh quotient associated with FCDA. Models found by the powered PILS methodology can contribute to reveal the relevance of particular predictors and often requires fewer and simpler components than their ordinary PILS counterparts. From the possibility of imposing restrictions on the powers available for optimization we obtain an explorative approach to predictive modeling not available to the traditional PILS methods. Copyright (C) 2008 John Wiley & Sons, Ltd.
partial least squares discriminant analysis fisher's canonical discriminant analysis ppls prior probabilities