Linear Multilayer Independent Component Analysis for Large Natural Scenes
Abstract
In this paper, linear multilayer ICA (LMICA) is proposed for extracting independent components from quite high-dimensional observed signals such as large-size natural scenes. There are two phases in each layer of LMICA. One is the mapping phase, where a one-dimensional mapping is formed by a stochastic gradient algorithm which makes more highly- correlated (non-independent) signals be nearer incrementally. Another is the local-ICA phase, where each neighbor (namely, highly-correlated) pair of signals in the mapping is separated by the MaxKurt algorithm. Because LMICA separates only the highly-correlated pairs instead of all ones, it can extract independent components quite efficiently from ap- propriate observed signals. In addition, it is proved that LMICA always converges. Some numerical experiments verify that LMICA is quite ef- ficient and effective in large-size natural image processing.
Cite
Text
Matsuda and Yamaguchi. "Linear Multilayer Independent Component Analysis for Large Natural Scenes." Neural Information Processing Systems, 2004.Markdown
[Matsuda and Yamaguchi. "Linear Multilayer Independent Component Analysis for Large Natural Scenes." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/matsuda2004neurips-linear/)BibTeX
@inproceedings{matsuda2004neurips-linear,
title = {{Linear Multilayer Independent Component Analysis for Large Natural Scenes}},
author = {Matsuda, Yoshitatsu and Yamaguchi, Kazunori},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {897-904},
url = {https://mlanthology.org/neurips/2004/matsuda2004neurips-linear/}
}