標題: | Model-Based Clustering by Probabilistic Self-Organizing Maps |
作者: | Cheng, Shih-Sian Fu, Hsin-Chia Wang, Hsin-Min 資訊工程學系 Department of Computer Science |
關鍵字: | Classification expectation-maximization (CEM) algorithm;deterministic annealing expectation-maximization (DAEM) algorithm;expectation-maximization (EM) algorithm;model-based clustering;probabilistic self-organizing map (PbSOM);self-organizing map (SOM) |
公開日期: | 1-五月-2009 |
摘要: | In this paper, we consider the learning process of a probabilistic self-organizing map (PbSOM) as a model-based data clustering procedure that preserves the topological relationships between data clusters in a neural network. Based on this concept, we develop a coupling-likelihood mixture model for the PbSOM that extends the reference vectors in Kohonen's self-organizing map (SOM) to multivariate Gaussian distributions. We also derive three expectation-maximization (EM)-type algorithms, called the SOCEM, SOEM, and SODAEM algorithms, for learning the model (PbSOM) based on the maximum-likelihood criterion. SOCEM is derived by using the classification EM (CEM) algorithm to maximize the classification likelihood; SOEM is derived by using the EM algorithm to maximize the mixture likelihood; and SODAEM is a deterministic annealing (DA) variant of SOCEM and SOEM. Moreover, by shrinking the neighborhood size, SOCEM and SOEM can be interpreted, respectively, as DA variants of the CEM and EM algorithms for Gaussian model-based clustering. The experimental results show that the proposed PbSOM learning algorithms achieve comparable data clustering performance to that of the deterministic annealing EM (DAEM) approach, while maintaining the topology-preserving property. |
URI: | http://dx.doi.org/10.1109/TNN.2009.2013708 http://hdl.handle.net/11536/7302 |
ISSN: | 1045-9227 |
DOI: | 10.1109/TNN.2009.2013708 |
期刊: | IEEE TRANSACTIONS ON NEURAL NETWORKS |
Volume: | 20 |
Issue: | 5 |
起始頁: | 805 |
結束頁: | 826 |
顯示於類別: | 期刊論文 |