Searching for authors named "Tobias Blaschke" – sorted by Relevance.
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Nonlinear blind source separation by integrating independent component analysis and slow feature analysis
- In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unknown nonlinear transformations of the sources using only the independence assumption. Integrating the objectives of statist
- Cited by 1 (0 self) – Add To MetaCart
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CuBICA: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization
- CuBICA, an improved method for independent component analysis (ICA) based on the diagonalization of cumulant tensors is proposed. It is based on Comon's algorithm [1994a] but it takes third- and fourth-order cumulant tensors into account simultaneously. The underlying contrast function is also mat
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What is the relation between slow feature analysis and independent component analysis
- The final version of this article has been published in
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Independent slow feature analysis and nonlinear blind source separation
- The final version of this article will be published in Neural Computation, published by The MIT Press. This version does not differ significantly from the final version.
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Blaschke, T. and Wiskott, L. (2003). CuBICA: Independent Component Analysis by Simultaneous
- CuBICA, an improved method for independent component analysis (ICA) based on the diagonalization of cumulant tensors is proposed. It is based on Comon's algorithm [1994a] but it takes third- and fourth-order cumulant tensors into account simultaneously. The underlying contrast function is also mat
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