TY - JOUR
T1 - Kernel principal angles for classification machines with applications to image sequence interpretation
AU - Wolf, Lior
AU - Shashua, Amnon
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=10044235880&partnerID=8YFLogxK
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AN - SCOPUS:10044235880
SN - 1063-6919
VL - 1
SP - I/635-I/640
JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
T2 - 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Y2 - 18 June 2003 through 20 June 2003
ER -