An algorithm exploits the near-Sylvester structure of meeting time equations to compute all pairwise expected meeting times on graphs in O(N^4) operations.
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MUDRA extends FLDA to multivariate time series with missing data via an ECM algorithm and shows improved classification over prior methods on the Articulary Word Recognition dataset.
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Meeting times on graphs in near-cubic time
An algorithm exploits the near-Sylvester structure of meeting time equations to compute all pairwise expected meeting times on graphs in O(N^4) operations.
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Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data
MUDRA extends FLDA to multivariate time series with missing data via an ECM algorithm and shows improved classification over prior methods on the Articulary Word Recognition dataset.