The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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Support Vector Machines are supervised regression and classification machines which have the nice property of automatically identifying which of the data points are most important in creating the machine. Kernel Principal Component Analysis (KPCA) is a related technique in that it also relies on linear operations in a feature space but does not have this ability to identify important points. Sparse KPCA goes too far in that it identifies a single data point as most important. We show how, by bagging the data, we may create a compromise which gives us a sparse but not grandmother representation for KPCA.