Grassmannian Spectral Regression for Learning and Classification.

Computational performance associated with high dimensional data is a common challenge for real-world action classification systems. Subspace learning, and manifold learning in particular, have received considerable attention as means of finding efficient low-dimensional representations that lead to better classification and efficient processing. A Grassmann manifold is a space that promotes smooth surfaces where points represent subspaces. In this paper, Grassmannian Spectral Regression (GRASP) is presented as a Grassmann inspired subspace learning algorithm that combines the benefits of Grassmann manifolds and spectral regression for fast and accurate classification. GRASP involves embedding high dimensional action subspaces as individual points onto a Grassmann manifold, kernelizing the embeddings onto a projection space, and then applying Spectral Regression for fast and accurate action classification. Furthermore, spatiotemporal action descriptions called Motion History Surfaces and Motion Depth Surfaces are utilized. The effectiveness of GRASP is illustrated for computationally intensive, multi-view and 3D action classification datasets. [ABSTRACT FROM AUTHOR]