Dimension reduction provides an important tool for preprocessing large scale data sets. A possible model for dimension reduction is realized by projecting onto the non-Gaussian part of a given multivariate recording. We prove that the subspaces of such a projection are unique given that the Gaussian subspace is of maximal dimension. This result therefore guarantees that projection algorithms uniquely recover the underlying lower dimensional data signals. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Theis, F. J., & Kawanabe, M. (2006). Uniqueness of non-Gaussian subspace analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 917–925). Springer Verlag. https://doi.org/10.1007/11679363_114
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