Learning LowDimensional Temporal Representations
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Proceedings of the 35th International Conference on Machine Learning, PMLR 80:47614770, 2018.
Abstract
Lowdimensional discriminative representations enhance machine learning methods in both performance and complexity, motivating supervised dimensionality reduction (DR) that transforms highdimensional 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 LTLDA to learn lowdimensional 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 easilyseparable 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 realworld sequence datasets to show the interest of our method.
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