Learning principal components in a contextual space
نویسنده
چکیده
Principal Components Analysis (PCA) consists in nding the orthogonal directions of highest variance in a distribution of vectors. In this paper, we propose to extract the principal components of a random vector that partially results from a previous PCA. We demonstrate that this contextual PCA pro vides an optimal linear encoding of temporal con text. A recurrent neural netw ork based on this principle is evaluated.
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