2 research outputs found
Estimating the Accuracy of Spectral Learning for HMMs
Hidden Markov models (HMMs) are usually learned using
the expectation maximisation algorithm which is, unfortunately, subject
to local optima. Spectral learning for HMMs provides a unique, optimal
solution subject to availability of a sufficient amount of data. However,
with access to limited data, there is no means of estimating the accuracy
of the solution of a given model. In this paper, a new spectral evaluation
method has been proposed which can be used to assess whether the
algorithm is converging to a stable solution on a given dataset. The
proposed method is designed for real-life datasets where the true model is
not available. A number of empirical experiments on synthetic as well as
real datasets indicate that our criterion is an accurate proxy to measure
quality of models learned using spectral learning