Abstract
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.
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Theis, F.J., Kawanabe, M. (2006). Uniqueness of Non-Gaussian Subspace Analysis. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_114
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DOI: https://doi.org/10.1007/11679363_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
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