Kernel Matching Pursuit

Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target function in the leastsquares sense. We show how matching pursuit can be extended to use non-squared error loss functions, and how it can be used to build kernel-based solutions to machine-learning problems, while keeping control of the sparsity of the solution. We also derive MDL motivated generalization bounds for this type of algorithm, and compare them to related SVM (Support Vector Machine) bounds. Finally, links to boosting algorithms and RBF training procedures, as well as an extensive experimental comparison with SVMs for classification are given, showing comparable results with typically sparser models.

• Type: Journal
• Year: 2002
• Where: ML
• Authors: Pascal Vincent, Yoshua Bengio
• Keywords: Algorithms, Basis Functions, Error Loss Functions, Machine Learning, ML 2002