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Subspace-based Learning with Grassmann Kernels

Subspace-based Learning with Grassmann Kernels

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. In this paper we propose a discriminant learning framework for problems in which data consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we can make learning algorithms adapt naturally to the problems with linear invariant structures. We propose a unifying view on the subspace-based learning method by formulating the problems on the Grassmann manifold, which is the set of fixed-dimensional subspaces of a Euclidean space. Previous methods on the problem typically adopt an inconsistent strategy: feature extraction is performed in the Euclidean space while non-Euclidean dissimilarity measures are used. In our approach, we treat each subspace as a point in... Show More
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