A canonical form for weighted automata and applications to approximate minimization
We study the problem of constructing approximations to a weighted automaton. Weighted finite automata (WFA) are closely related to the theory of rational series. A rational series is a function from strings to real numbers that can be computed by a WFA. This includes probability distributions generated by hidden Markov models and probabilistic automata. The relationship between rational series and WFA is analogous to the relationship between regular languages and ordinary automata. Associated with such rational series are infinite matrices called Hankel matrices which play a fundamental role in the theory of minimal WFA. In this talk I describe: (1) an effective procedure for computing the singular value decomposition (SVD) of such infinite Hankel matrices based on their finite representation in terms of WFA; (2) a new canonical form for WFA based on this SVD decomposition; and, (3) an algorithm to construct approximate minimizations of a given WFA. The goal of the approximate minimization algorithm is to start from a minimal WFA and produce a smaller WFA that is close to the given one in a certain sense. The desired size of the approximating automaton is given as input. I will give bounds describing how well the approximation emulates the behavior of the original WFA.