Sequential adaptive compressed sampling via Huffman codes Export

@article{citeulike:5015439, abstract = {There are two main approaches in compressed sensing: the geometric approach and the combinatorial approach. In this paper we introduce an information theoretic approach and use results from the theory of Huffman codes to construct a sequence of binary sampling vectors to determine a sparse signal. Unlike other approaches, our approach is adaptive in the sense that each sampling vector depends on the previous sample. The number of measurements we need for a k-sparse vector in n-dimensional space is no more than O(k log n) and the reconstruction is O(k).}, archivePrefix = {arXiv}, author = {Aldroubi, Akram and Wang, Haichao and Zarringhalam, Kourosh}, citeulike-article-id = {5015439}, citeulike-linkout-0 = {http://arxiv.org/abs/0810.4916}, citeulike-linkout-1 = {http://arxiv.org/pdf/0810.4916}, day = {25}, eprint = {0810.4916}, keywords = {compressed\_sensing, compression, sequential\_decisions}, month = {Jun}, posted-at = {2009-07-02 02:27:28}, priority = {2}, title = {Sequential adaptive compressed sampling via Huffman codes}, url = {http://arxiv.org/abs/0810.4916}, year = {2009} }

TY - JOUR ID - citeulike:5015439 L3 - citeulike-article-id:5015439 N2 - There are two main approaches in compressed sensing: the geometric approach and the combinatorial approach. In this paper we introduce an information theoretic approach and use results from the theory of Huffman codes to construct a sequence of binary sampling vectors to determine a sparse signal. Unlike other approaches, our approach is adaptive in the sense that each sampling vector depends on the previous sample. The number of measurements we need for a k-sparse vector in n-dimensional space is no more than O(k log n) and the reconstruction is O(k). TI - Sequential adaptive compressed sampling via Huffman codes KW - compressed_sensing KW - compression KW - sequential_decisions AU - Aldroubi, Akram AU - Wang, Haichao AU - Zarringhalam, Kourosh PY - 2009/Jun/25/ UR - http://arxiv.org/abs/0810.4916 ER -