2008/10/27 by Akram Aldroubi, Aldroubi, Akram, Haichao Wang +3 · 2 citations
Computer Science · Engineering · #Blind Source Separation Techniques #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.0810.4916
openalex publication_date 2008/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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).