Independent Subspace Analysis Using Geodesic Spanning Trees
Abstract
A novel algorithm for performing Independent Subspace Analysis, the estimation of hidden independent subspaces is introduced. This task is a generalization of Independent Component Analysis. The algorithm works by estimating the multi-dimensional differential entropy. The estimation utilizes minimal geodesic spanning trees matched to the sample points. Numerical studies include (i) illustrative examples, (ii) a generalization of the cocktail-party problem to songs played by bands, and (iii) an example on mixed independent subspaces, where subspaces have dependent sources, which are pairwise independent.
Cite
Text
Póczos and Lörincz. "Independent Subspace Analysis Using Geodesic Spanning Trees." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102436Markdown
[Póczos and Lörincz. "Independent Subspace Analysis Using Geodesic Spanning Trees." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/poczos2005icml-independent/) doi:10.1145/1102351.1102436BibTeX
@inproceedings{poczos2005icml-independent,
title = {{Independent Subspace Analysis Using Geodesic Spanning Trees}},
author = {Póczos, Barnabás and Lörincz, András},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {673-680},
doi = {10.1145/1102351.1102436},
url = {https://mlanthology.org/icml/2005/poczos2005icml-independent/}
}