Stream Derivation and Clustering Scheme for Subspace Distribution Clustering Hidden Markov Model
نویسندگان
چکیده
In [1], our novel subspace distribution clustering hidden Markov model (SDCHMM) made its debut as an approximation to continuous density HMM (CDHMM). Deriving SDCHMMs from CDHMMs requires a definition of multiple streams and a Gaussian clustering scheme. Previously we have tried 4 and 13 streams, which are common but ad hoc choices. Here we present a simple and coherent definition for streams of any dimension: the streams comprise the most correlated features. The new definition is shown to give better performance in two recognition tasks. The clustering scheme in [1] is an O(n2) algorithm which can be slow when the number of Gaussians in the original CDHMMs is large. Now we have devised a modified k-means clustering scheme using the Bhattacharyya distance as the distance measure between Gaussian clusters. Not only is the new clustering scheme faster, when combined with the new stream definitions, we now obtain SDCHMMs which perform at least as well as the original CDHMMs (with better results in some cases).
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