Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Pedro Felzenszwalb, Daniel Huttenlocher, Jon Kleinberg


In applying Hidden Markov Models to the analysis of massive data streams, it is often necessary to use an arti(cid:12)cially reduced set of states; this is due in large part to the fact that the basic HMM estimation algorithms have a quadratic dependence on the size of the state set. We present algorithms that reduce this computational bottleneck to linear or near-linear time, when the states can be embedded in an underlying grid of parameters. This type of state representation arises in many domains; in particular, we show an application to tra(cid:14)c analysis at a high-volume Web site.