The Information Bottleneck EM algorithm

@misc{citeulike:1866475, abstract = {Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation Maximization (EM) algorithm. This algorithm, however, can get trapped in local maxima. In this paper we explore a new approach that is based on the Information Bottleneck principle.}, author = {Elidan, G. and Friedman, N. }, citeulike-article-id = {1866475}, keywords = {continuation-methods, probabilistic-models, statistics}, posted-at = {2008-01-09 19:12:25}, priority = {3}, title = {The Information Bottleneck EM algorithm}, url = {http://citeseer.ist.psu.edu/586035.html}, year = {2003} }

TY - GEN ID - citeulike:1866475 L3 - citeulike-article-id:1866475 TI - The Information Bottleneck EM algorithm N2 - Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation Maximization (EM) algorithm. This algorithm, however, can get trapped in local maxima. In this paper we explore a new approach that is based on the Information Bottleneck principle. KW - continuation-methods KW - probabilistic-models KW - statistics AU - Elidan, G AU - Friedman, N PY - 2003/// UR - http://citeseer.ist.psu.edu/586035.html ER -