@inproceedings{afbbd8d71ff24e728d479b343954fa6b,
title = "Uniqueness of non-Gaussian subspace analysis",
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.",
author = "Theis, {Fabian J.} and Motoaki Kawanabe",
year = "2006",
doi = "10.1007/11679363_114",
language = "English",
isbn = "3540326308",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "917--925",
booktitle = "Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings",
note = "6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 ; Conference date: 05-03-2006 Through 08-03-2006",
}