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• W2103447716 abstract "In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning into a single framework that aims to learn slow varying parts-based representations of time varying sequences. We demonstrate that the proposed algorithm arises naturally by embedding the Slow Features Analysis trace optimization problem in the nonnegative subspace learning framework and derive novel multiplicative update rules for its optimization. The usefulness of the developed algorithm is demonstrated for unsupervised facial behaviour dynamics analysis on MMI database." @default.
• W2103447716 created "2016-06-24" @default.
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• W2103447716 date "2014-10-01" @default.
• W2103447716 modified "2023-09-23" @default.
• W2103447716 title "Slow features nonnegative matrix factorization for temporal data decomposition" @default.
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• W2103447716 doi "https://doi.org/10.1109/icip.2014.7025286" @default.
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