We propose a method based on sparse representation
(SR) to cluster data drawn from multiple low-dimensional
linear or affine subspaces embedded in a high-dimensional
space. Our ...
Bottom-up segmentation tends to rely on local features. Yet, many natural and man-made objects contain repeating elements. Such structural and more spread-out features are importa...
Compressed domain image processing techniques are becoming increasingly important. Compressed domain retrieval It allows the calculation of image features and hence content-based ...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to the optimal solution when used with lookup tables. It is shown, however, that ...
For a Boolean formula on n variables, the associated property P is the collection of n-bit strings that satisfy . We study the query complexity of tests that distinguish (with hig...
Eli Ben-Sasson, Prahladh Harsha, Sofya Raskhodniko...