Abstract
A novel algorithm called independent subspace analysis (ISA) is introduced to estimate independent subspaces. The algorithm solves the ISA problem by estimating multi-dimensional differential entropies. Two variants are examined, both of them utilize distances between the k-nearest neighbors of the sample points. Numerical simulations demonstrate the usefulness of the algorithms.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Póczos, B., Lőrincz, A. (2005). Independent Subspace Analysis Using k-Nearest Neighborhood Distances. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_27
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DOI: https://doi.org/10.1007/11550907_27
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