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Learning over Sets using Kernel Principal Angles

2003
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Journal of machine learning research
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We consider the problem of learning with instances defined over a space of sets of vectors. We derive a new positive definite kernel f (A, B) defined over pairs of matrices A, B based on the concept of principal angles between two linear subspaces. We show that the principal angles can be recovered using only inner-products between pairs of column vectors of the input matrices thereby allowing the original column vectors of A, B to be mapped onto arbitrarily high-dimensional feature spaces. We

dblp:journals/jmlr/WolfS03
fatcat:gd2ytc37nfagllkl6fbalpt2fu