Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inferenc...

In this paper, we propose EMACF (Expectation-Maximization Algorithm for Clustering Features) to generate clusters from data summaries rather than data items directly. Incorporating with an adaptive grid-based data summarization procedure, we establish a scalable clustering algorithm: gEMACF. The experimental results show that gEMACF can generate more accurate results than other scalable cluster...

あらまし 本論文では,複数の楽器音が混在したモノラルの音楽音響信号に対して,メロディーとベースの音 高(基本周波数)を推定する手法を提案する.従来の音高推定手法や音源分離手法は,たかだか三つの音の混合音 しか扱うことができず,市販の CDによるジャズやポピュラー音楽の音響信号には有効に機能しなかった.本手 法は,混合音下で安定に抽出できない基本周波数成分には依存せず,意図的に制限した周波数帯域(メロディー は中高域,ベースは低域)にある高調波成分が支持する最も優勢な音高を求める.その際,音源数を仮定せずに あらゆる音高の高調波構造が混在しているとみなして混合音をモデル化し,EM(Expectation-Maximization) アルゴリズムにより各高調波構造が相対的にどれくらい優勢かを推定する.更に,マルチエージェントモデルを 導入し,各エージェントが音高の時間的な軌跡を追跡するこ...

We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The NEM theorem shows that injected noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity condition holds. The gener...

We propose a Tabu search based Expectation Maximization (EM) algorithm for image segmentation in an unsupervised frame work. Hidden Markov Random Field (HMRF) model is used to model the images. The observed image is considered to be a realization of Gaussian Hidden Markov Random Field (GHMRF) model. The segmentation problem is formulated as a pixel labeling problem. The GHMRF model parameters a...

In this work, we introduce a novel estimator of the predictive risk with Poisson data, when loss function is Kullback-Leibler divergence, in order to define regularization parameter's choice rule for Expectation Maximization (EM) algorithm. To aim, prove counterpart Stein's Lemma Gaussian variables, and from result derive proposed showing its analogies well-known Unbiased Risk Estimator valid q...