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This paper proposes a novel algorithm for signal classification problems. Weconsider a non-stationary random signal, where samples can be classified intoseveral different classes, and samples in each class are identicallyindependently distributed with an unknown probability distribution. The problemto be solved is to estimate the probability distributions of the classes andthe correct membership of the samples to the classes. We propose a signalclassification method based on the data compression principle that the accurateestimation in the classification problems induces the optimal signal models fordata compression. The method formulates the classification problem as anoptimization problem, where a so called {"classification gain"} is maximized.In order to circumvent the difficulties in integer optimization, we propose acontinuous relaxation based algorithm. It is proven in this paper thatasymptotically vanishing optimality loss is incurred by the continuousrelaxation. We show by simulation results that the proposed algorithm iseffective, robust and has low computational complexity. The proposed algorithmcan be applied to solve various multimedia signal segmentation, analysis, andpattern recognition problems.

Novel Blind Signal Classification Method Based on Data Compression