K
Klaus Nordhausen
Researcher at Vienna University of Technology
Publications - 149
Citations - 6769
Klaus Nordhausen is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Independent component analysis & Blind signal separation. The author has an hindex of 26, co-authored 138 publications receiving 6201 citations. Previous affiliations of Klaus Nordhausen include RMIT University & University of Tampere.
Papers
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Journal ArticleDOI
Asymptotic and bootstrap tests for subspace dimension
TL;DR: In this paper, three popular linear dimension reduction methods, namely principal component analysis (PCA), fourth order blind identification (FOBI), and sliced inverse regression (SIR), are considered in detail and the first two moments of subsets of the eigenvalues are used to test for the dimension of the signal space.
Journal ArticleDOI
Computing the Oja Median in R: The Package OjaNP
TL;DR: The Oja median is one of several extensions of the univariate median to the multivariate case as discussed by the authors, which has many nice properties, but is computationally demanding, and it has been shown to be computationally inefficient.
Journal ArticleDOI
Minimum distance index for complex valued ICA
TL;DR: The Minimum Distance index is generalized to be applicable in complex valued ICA and a complex version of AMUSE is presented and compared to complex FOBI in a simulation study.
Posted Content
Averaging orthogonal projectors
TL;DR: This work proposes a generalization of the Crone and Crosby distance, a weighted distance that allows to combine subspaces of different dimensions to combine various dimension reduction methods.
Book ChapterDOI
Non-Gaussian Component Analysis: Testing the Dimension of the Signal Subspace
Una Radojicic,Klaus Nordhausen +1 more
TL;DR: It is shown that the simultaneous use of two scatter functionals can be used for this purpose and a bootstrap test is suggested to test the dimension of the non-Gaussian subspace.