@inproceedings{51dd92fc73984cf9909ca2bb1b3228f0,
title = "Algorithms for independent components analysis and higher order statistics",
abstract = "A latent variable generative model with finite noise is used to decribe several different algorithms for Independent Components Analysis (ICA). In particular, the Fixed Point ICA algorithm is shown to be equivalent to the Expectation-Maximization algorithm for maximum likelihood under certain constraints, allowing the conditions for global convergence to be elucidated. The algorithms can also be explained by their generic behavior near a singular point where the size of the optimal generative bases vanishes. An expansion of the likelihood about this singular point indicates the role of higher order correlations in determining the features discovered by ICA. The application and convergence of these algorithms are demonstrated on a simple illustrative example.",
author = "Lee, \{Daniel D.\} and Uri Rokni and Haim Sompolinsky",
year = "2000",
language = "אנגלית",
isbn = "0262194503",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
pages = "491--497",
booktitle = "Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999",
note = "13th Annual Neural Information Processing Systems Conference, NIPS 1999 ; Conference date: 29-11-1999 Through 04-12-1999",
}