A selective sampling procedure called distilled sensing (DS) is proposed, and shown to be an effective method for recovering sparse signals in noise. Based on the notion that it is often easier to rule out locations that do not contain signal than it is to directly identify non-zero signal components, DS is a sequential method that systematically focuses sensing resources towards the signal sub...

A new dictionary learning method for exact sparse representation is presented in this paper. As the dictionary learning methods often iteratively update the sparse coefficients and dictionary, when the approximation error is small or zero, algorithm convergence will be slow or non-existent. The proposed framework can be used in such a setting by gradually increasing the fidelity of the approxim...

Randomized partition trees have recently been shown to be very effective in solving nearest neighbor search problem. In spite of enjoying strong theoretical guarantee, it suffers from high space complexity, since each internal node of the tree needs to store a d dimensional projection direction leading to aO(nd) space complexity for a dataset of size n. Inspired by the fast Johnson-Lindenstraus...