Novel Blind Signal Classification Method Based on Data Compression
Xudong Ma
备注：
Camera ready version, accepted for publication in Proc. the 6th International Conference on Information Technology : New Generations, ITNG 2009, Las Vegas, Nevada, USA. Typos in the reference list have been fixed
发表信息：
Proceeding of the 6th International Conference on Information Technology : New Generations, Las Vegas, Nevada, April 27-29, 2009
This paper proposes a novel algorithm for signal classification problems. We consider a non-stationary random signal, where samples can be classified into several different classes, and samples in each class are identically independently distributed with an unknown probability distribution. The problem to be solved is to estimate the probability distributions of the classes and the correct membership of the samples to the classes. We propose a signal classification method based on the data compression principle that the accurate estimation in the classification problems induces the optimal signal models for data compression. The method formulates the classification problem as an optimization problem, where a so called {"classification gain"} is maximized. In order to circumvent the difficulties in integer optimization, we propose a continuous relaxation based algorithm. It is proven in this paper that asymptotically vanishing optimality loss is incurred by the continuous relaxation. We show by simulation results that the proposed algorithm is effective, robust and has low computational complexity. The proposed algorithm can be applied to solve various multimedia signal segmentation, analysis, and pattern recognition problems.
Cross-Domain Lossy Compression via Constrained Minimum Entropy Coupling
School of Electrical Engineering（电气工程学院）；Computer Science Oregon State University Corvallis, OR, 97331 Email（计算机科学，俄勒冈州立大学，科瓦利斯，OR，97331 邮箱）；Computer Science, Oregon State University, Corvallis, OR, 97331（计算机科学，俄勒冈州立大学，科瓦利斯，OR，97331）
