Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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Kernelizing partial least squares (PLS), an algorithm which has been particularly popular in chemometrics, leads to kernel PLS which has several interesting properties, including a sub-cubic runtime for learning, and an iterative construction of directions which are relevant for predicting the outputs. We show that the kernelization of PLS introduces interesting properties not found in ordinary PLS, giving novel insights into the workings of kernel PLS and the connections to kernel ridge regression and conjugate gradient descent methods. Furthermore, we show how to correctly define the degrees of freedom for kernel PLS and how to efficiently compute an unbiased estimate. Finally, we address the practical problem of model selection. We demonstrate how to use the degrees of freedom estimate to perform effective model selection, and discuss how to implement crossvalidation schemes efficiently.