Learning principal components in a contextual space.
Thomas Voegtlin · 2000
Principal Components Analysis (PCA) consists in finding 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 provides an optimal linear encoding of temporal context. A recurrent neural network based on this principle is evaluated.