  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.
