Statistical signal processing with nonnegativity constraints
Nonnegativity constraints arise frequently in statistical learning and pattern recognition. Multiplicative updates provide natural solutions to optimizations involving these constraints. One well known set of multiplicative updates is given by the Expectation-Maximization algorithm for hidden Markov models, as used in automatic speech recognition. Recently, we have derived similar algorithms for nonnegative deconvolution and nonnegative quadratic programming. These algorithms have applications to low-level problems in voice processing, such as fundamental frequency estimation, as well as high-level problems, such as the training of large margin classifiers. In this paper, we describe these algorithms and the ideas that connect them. Disciplines Computer Engineering | Computer Sciences Comments Proceedings of the Eighth European Conference on Speech Communication and Technology, volume 2, pages 1001-1004, held 1-4 September 2003 in Geneva, Switzerland. This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/13 Statistical Signal Processing with Nonnegativity Constraints Lawrence K. Saul, Fei Sha, Daniel D. Lee Department of Computer and Information Science Department of Electrical and System Engineering University of Pennsylvania, Philadephia, PA {lsaul,feisha,ddlee}@seas.upenn.edu