Kernel principal angles for classification machines with applications to image sequence interpretation
Wof, L.
Shashua, A.
Sch. of Comput. Sci. & Eng., Hebrew Univ. of Jerusalem, Israel;
Abstract
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 apply this technique to inference over image sequences applications of face recognition and irregular motion trajectory detection.
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