Independent Components Analysis Through Product Density Estimation
Abstract
We present a simple direct approach for solving the ICA problem, using density estimation and maximum likelihood. Given a candi(cid:173) date orthogonal frame, we model each of the coordinates using a semi-parametric density estimate based on cubic splines. Since our estimates have two continuous derivatives, we can easily run a sec(cid:173) ond order search for the frame parameters. Our method performs very favorably when compared to state-of-the-art techniques.
Cite
Text
Hastie and Tibshirani. "Independent Components Analysis Through Product Density Estimation." Neural Information Processing Systems, 2002.Markdown
[Hastie and Tibshirani. "Independent Components Analysis Through Product Density Estimation." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/hastie2002neurips-independent/)BibTeX
@inproceedings{hastie2002neurips-independent,
title = {{Independent Components Analysis Through Product Density Estimation}},
author = {Hastie, Trevor and Tibshirani, Rob},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {665-672},
url = {https://mlanthology.org/neurips/2002/hastie2002neurips-independent/}
}