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CORR
2010
Springer

Pattern Classification In Symbolic Streams via Semantic Annihilation of Information

13 years 11 months ago
Pattern Classification In Symbolic Streams via Semantic Annihilation of Information
We propose a technique for pattern classification 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 subset of probabilistic finite state automata (PFSA), and the group sum is used to formulate pattern-specific semantic annihilators. The annihilators attempt to identify pre-specified patterns via removal of essentially all inter-symbol correlations from observed sequences, thereby turning them into symbolic white noise. Thus a perfect annihilation corresponds to a perfect 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.
Ishanu Chattopadhyay, Yicheng Wen, Asok Ray
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CORR
Authors Ishanu Chattopadhyay, Yicheng Wen, Asok Ray
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