Measuring the Influence of Observations in HMMs through the Kullback-Leibler Distance
This work addresses the specific problem of outlier detection in HMMs for researchers and practitioners in fields like signal processing or bioinformatics, but it is incremental as it builds on existing HMM and KLD methods.
The paper tackles the problem of measuring the influence of individual observations on hidden states in Hidden Markov Models (HMMs) by using the Kullback-Leibler distance, and introduces a linear complexity algorithm for this computation, applying it to outlier detection in HMM data series.
We measure the influence of individual observations on the sequence of the hidden states of the Hidden Markov Model (HMM) by means of the Kullback-Leibler distance (KLD). Namely, we consider the KLD between the conditional distribution of the hidden states' chain given the complete sequence of observations and the conditional distribution of the hidden chain given all the observations but the one under consideration. We introduce a linear complexity algorithm for computing the influence of all the observations. As an illustration, we investigate the application of our algorithm to the problem of detecting outliers in HMM data series.