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OwnerLawrence Carin, John Paisely +3inventors
InventorsLawrence Carin, John Paisely, Yuting Qi, Xuejun Liao, Qiuhua Liu
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A method of quantifying similarities between sequential data streams typically includes providing a pair of sequential data streams, designing a Hidden Markov Model (HMM) of at least a portion of each stream; and computing a quantitative measure of similarity between the streams using the HMMs. For a plurality of sequential data streams, a matrix of quantitative measures of similarity may be created. A spectral analysis may be performed on the matrix of quantitative measure of similarity matrix to define a multi-dimensional diffusion space, and the plurality of sequential data streams may be graphically represented and/or sorted according to the similarities therebetween. In addition, semi-supervised and active learning algorithms may be utilized to learn a user's preferences for data streams and recommend additional data streams that are similar to those preferred by the user. Multi-task learning algorithms may also be applied.
A method of quantifying similarities between sequential data streams, the method comprising:
A method of representing similarities between a plurality of sequential data streams, the method comprising:
A system for quantifying and representing similarities between sequential data streams, the system comprising:
A system for searching a plurality of data streams, the system comprising:
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