Pattern Classification in symbolic streams via semantic annihilation of information

We propose a technique for pattern identification in symbolic streams via selective erasure of observed symbols, in cases where the patterns of interest are represented as Probabilistic Finite State Automata (PFSA). We define an additive abelian group for a slightly restricted set of probabilistic machines, and the group sum is used to formulate pattern-specific semantic annihilators. The annihilators attempt to identify pre-specified patterns via removal of inter-symbol correlations from observed sequences, thereby turning them into symbolic white noise. Thus a perfect annihilation corresponds to a pattern match. This approach of classification via information annihilation is shown to be strictly advantageous, with theoretical guarantees, for a large class of PFSA models. The results are supported by simulation experiments.