Baktashmotlagh, Mahsa; Harandi, Mehrtash; Lovell, Brian C; Salzmann, Mathieu
Low-dimensional representations are key to the success of many video classification algorithms. However, the commonly-used dimensionality reduction techniques fail to account for the fact that only part of the signal is shared across all the videos in one class. As a consequence, the resulting representations contain instance-specific information, which introduces noise in the classification process. In this paper, we introduce non-linear stationary subspace analysis: a method that overcomes...[Show more]
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