Novel Blind Signal Classification Method Based on Data Compression

TL;DR

This paper introduces a data compression-based algorithm for classifying non-stationary signals, effectively estimating class distributions and memberships with low complexity and high robustness.

Contribution

It presents a novel classification method using data compression principles, formulating the problem as an optimization with a continuous relaxation approach.

Findings

Effective and robust classification demonstrated through simulations

Low computational complexity of the proposed algorithm

Asymptotic optimality with continuous relaxation

Abstract

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.