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Low-dimensional discriminative representations enhance machine learning methods in both performance and complexity, motivating supervised dimensionality reduction (DR) that transforms high-dimensional data to a discriminative subspace. Most DR methods require data to be i.i.d., however, in some domains, data naturally come in sequences, where the observations are temporally correlated. We propose a DR method called LT-LDA to learn low-dimensional temporal representations. We construct the separability among sequence classes by lifting the holistic temporal structures, which are established based on temporal alignments and may change in different subspaces. We jointly learn the subspace and the associated alignments by optimizing an objective which favors easily-separable temporal structures, and show that this objective is connected to the inference of alignments, thus allows an iterative solution. We provide both theoretical insight and empirical evaluation on real-world sequence datasets to show the interest of our method.
Author Information
Bing Su (Institute of Software, Chinese Academy of Sciences)
Ying Wu (Northwestern University)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Learning Low-Dimensional Temporal Representations »
Fri Jul 13th 08:20 -- 08:30 AM Room K11
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2019 Poster: Learning Distance for Sequences by Learning a Ground Metric »
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