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 language | English |
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| Pages (from-to) | I/635-I/640 |
| Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Volume | 1 |
| State | Published - 2003 |
| Externally published | Yes |
| Event | 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Madison, WI, United States Duration: 18 Jun 2003 → 20 Jun 2003 |