The basic idea behind Expectation-Maximization
نویسنده
چکیده
3 The Expectation-Maximization algorithm 7 3.1 Jointly-non-concave incomplete log-likelihood . . . . . . . . . . . 7 3.2 (Possibly) Concave complete data log-likelihood . . . . . . . . . . 8 3.3 The general EM derivation . . . . . . . . . . . . . . . . . . . . . 10 3.4 The E& M-steps . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5 The EM algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.6 Will EM converge? . . . . . . . . . . . . . . . . . . . . . . . . . . 13
منابع مشابه
The basic idea of Expectation-Maximization
3 The Expectation-Maximization algorithm 7 3.1 Jointly-non-concave incomplete log-likelihood . . . . . . . . . . . 7 3.2 (Possibly) Concave complete data log-likelihood . . . . . . . . . . 8 3.3 The general EM derivation . . . . . . . . . . . . . . . . . . . . . 10 3.4 The E& M-steps . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5 The EM algorithm . . . . . . . . . . . . . . . . . . ...
متن کاملExpectation Maximization
The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...
متن کاملSimilarity based impulsive noise removal in color images
In this paper a novel approach to the problem of impulsive noise removal in color images based on the nonparametric density estimation is presented. The basic idea behind the new image filtering technique is the maximization of the similarities between pixels in a predefined filtering window. The new method is faster than the standard vector median filter and preserves better edges and fine ima...
متن کاملMedian-LVQ for Classification of Dissimilarity Data based on ROC-Optimization
In this article we consider a median variant of the learning vector quantization (LVQ) classifier for classification of dissimilarity data. However, beside the median aspect, we propose to optimize the receiver-operating characteristics (ROC) instead of the classification accuracy. In particular, we present a probabilistic LVQ model with an adaptation scheme based on a generalized ExpectationMa...
متن کاملThe Basic Idea of EM
4 The Expectation-Maximization algorithm 7 4.1 Jointly-non-concave incomplete log-likelihood . . . . . . . . . . . 7 4.2 (Possibly) Concave complete data log-likelihood . . . . . . . . . . 8 4.3 The general EM derivation . . . . . . . . . . . . . . . . . . . . . 9 4.4 The E& M-steps . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.5 The EM algorithm . . . . . . . . . . . . . . . . . . ....
متن کامل