Confronted with the high-dimensional tensor-like visual data, we derive a method for the decomposition of an observed tensor into a low-dimensional structure plus unbounded but spa...
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Robustness is one of the most critical issues in the appearance-based learning strategies. In this work, we propose a novel kernel that is robust against data corruption for vario...
The task of computingbinary prefix sums (BPS, for short) arises, for example, in expression evaluation, data and storage compaction, and routing. This paper describes a scalable V...
Rong Lin, Koji Nakano, Stephan Olariu, Maria Crist...
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in...