Discriminative Dimensionality Reduction for Multi-Dimensional Sequences.

IEEE Transactions on Pattern Analysis and Machine Intelligence(2018)

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摘要
Since the observables at particular time instants in a temporal sequence exhibit dependencies, they are not independent samples. Thus, it is not plausible to apply i.i.d. assumption-based dimensionality reduction methods to sequence data. This paper presents a novel supervised dimensionality reduction approach for sequence data, called Linear Sequence Discriminant Analysis (LSDA). It learns a line...
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关键词
Hidden Markov models,Computational modeling,Sequences,Data models,Electronic mail,Analytical models,Time series analysis
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