Learning methods for kernel subspace methods and their applications

点击翻译按钮获取中文摘要

We have extended the subspace methods to feature extraction methods by constrained approximation problems. The subspace methods are sorts of realizations of the constrained approximation framework. The subspace methods have the rank constraint, however, by replacing the constraint to the other constraints, feature extractors that have various properties can be realized. We also have proposed the subset kernel principal component analysis to avoid large calculations in kernelization.