On Convergence Properties of the EM Algorithm for Gaussian Mixtures Export

@article{citeulike:1396412, abstract = {We build up the mathematical connection between the \"Expectation-Maximization\" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P , and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface....}, author = {Xu, Lei and Jordan, Michael I.}, citeulike-article-id = {1396412}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.7857}, journal = {Neural Computation}, keywords = {em, mixture}, number = {1}, pages = {129--151}, posted-at = {2007-06-18 10:35:45}, priority = {0}, title = {On Convergence Properties of the EM Algorithm for Gaussian Mixtures}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.7857}, volume = {8}, year = {1996} }

TY - JOUR ID - citeulike:1396412 L3 - citeulike-article-id:1396412 N2 - We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P , and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface.... IS - 1 JF - Neural Computation EP - 151 TI - On Convergence Properties of the EM Algorithm for Gaussian Mixtures VL - 8 SP - 129 KW - em KW - mixture AU - Xu, Lei AU - Jordan, Michael PY - 1996/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.7857 ER -