This paper presents a novel dimension reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-c...
Senjian An, Wanquan Liu, Svetha Venkatesh, Ronny T...
Within the field of action recognition, features and descriptors are often engineered to be sparse and invariant to transformation. While sparsity makes the problem tractable, it ...
Subspace learning is very important in today's world of information overload. Distinguishing between categories within a subset of a large data repository such as the web and ...
Nandita Tripathi, Michael P. Oakes, Stefan Wermter
Probabilistic databases hold promise of being a viable means for large-scale uncertainty management, increasingly needed in a number of real world applications domains. However, q...
This paper proposes a discriminative framework for efficiently aligning images. Although conventional Active Appearance Models (AAM)-based approaches have achieved some success, t...