Kernel principal angles for classification machines with applications to image sequence interpretation

Lior Wolf*, Amnon Shashua

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

80 Scopus citations

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.

Original languageAmerican English
Pages (from-to)I/635-I/640
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
StatePublished - 2003
Event2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Madison, WI, United States
Duration: 18 Jun 200320 Jun 2003

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