TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models

Reddy, Chandan K.; Chiang, Hsiao‐Dong; Rajaratnam, Bala

doi:10.1109/tpami.2007.70775

Cited by 42 publications

(15 citation statements)

References 30 publications

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“…It is well-known that care is required when initializing any EM algorithm. If the initialization is not carefully performed, then the EM algorithm may lead to unsatisfactory results (See for example Biernacki, Celeux, & Govaert, 2003;Reddy, Chiang, & Rajaratnam, 2008;Yang, Lai, & Lin, 2012 for discussions). Thus, fitting regression mixture models with the standard EM algorithm may yield poor estimations if the model parameters are not initialized properly.…”

Section: Regularized Regression Mixtures For Functional Datamentioning

confidence: 99%

“…Several approaches have been proposed in the literature in order to overcome the initialization problem, and to make the EM algorithm for Gaussian mixture models robust to initialization (e.g., Biernacki et al, 2003;Reddy et al, 2008;Yang et al, 2012). Further details about choosing starting values for the EM algorithm for Gaussian mixtures can be found in Biernacki et al (2003).…”

Section: Regularized Regression Mixtures For Functional Datamentioning

confidence: 99%

See 1 more Smart Citation

Model‐based clustering and classification of functional data

Chamroukhi

Nguyen

2019

WIREs Data Min & Knowl

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Complex data analysis is a central topic of modern statistics and learning systems which is becoming of broader interest with the increasing prevalence of high‐dimensional data. The challenge is to develop statistical models and autonomous algorithms that are able to discern knowledge from raw data, which can be achieved through clustering techniques, or to make predictions of future data via classification techniques. Latent data models, including mixture model‐based approaches, are among the most popular and successful approaches in both supervised and unsupervised learning. Although being traditional tools in multivariate analysis, they are growing in popularity when considered in the framework of functional data analysis (FDA). FDA is the data analysis paradigm in which each datum is a function, rather than a real vector. In many areas of application, including signal and image processing, functional imaging, bioinformatics, etc., the analyzed data are indeed often available in the form of discretized values of functions, curves, or surfaces. This functional aspect of the data adds additional difficulties when compared to classical multivariate data analysis. We review and present approaches for model‐based clustering and classification of functional data. We present well‐grounded statistical models along with efficient algorithmic tools to address problems regarding the clustering and the classification of these functional data, including their heterogeneity, missing information, and dynamical hidden structures. The presented models and algorithms are illustrated via real‐world functional data analysis problems from several areas of application. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Algorithmic Development > Statistics Technologies > Structure Discovery and Clustering

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Section: Regularized Regression Mixtures For Functional Datamentioning

confidence: 99%

Section: Regularized Regression Mixtures For Functional Datamentioning

confidence: 99%

Model‐based clustering and classification of functional data

Chamroukhi

Nguyen

2019

WIREs Data Min & Knowl

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“…A large number of local models are firstly identified in [18], and it selects the most separated ones. In [19], the TRUST-TECH technique is used to estimate adjacent local maximum values of the log-likelihood. en, Nasios and Bors [20] adopted a hierarchical maximum initialization to initialize the hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering

Zhang

et al. 2019

International Journal of Antennas and Propagation

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In this paper, the Gaussian mixture model (GMM) is introduced to the channel multipath clustering. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters. However, the EM widely converges into local optimization. To address this issue, a hybrid differential evolution (DE) and EM (DE-EM) algorithms are proposed in this paper. To be specific, the DE is employed to initialize the GMM parameters. Then, the parameters are estimated with the EM algorithm. Thanks to the global searching ability of DE, the proposed hybrid DE-EM algorithm is more likely to obtain the global optimization. Simulations demonstrate that our proposed DE-EM clustering algorithm can significantly improve the clustering performance.

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“…Applying traditional clustering techniques, such as k-means and hierarchical clustering, will not capture such co-clusters [10], [16], [22], [4], [32]. However, co-clustering (or biclustering) 1 has been proposed to simultaneously cluster both dimensions of a data matrix by utilizing the relationship between the two entities [10], [16], [26], [34], [27].…”

Section: Introductionmentioning

confidence: 99%

Efficient mining of discriminative co-clusters from gene expression data

Odibat

Reddy

2013

Knowl Inf Syst

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Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns in labeled datasets. In certain biological applications such as gene expression analysis, it is critical to consider the discriminative patterns that are correlated only in a subset of the feature space. The objective of this paper is two-fold: first, it presents an algorithm to efficiently find arbitrarily positioned co-clusters from complex data. Second, it extends this co-clustering algorithm to discover discriminative co-clusters by incorporating the class information into the co-cluster search process. In addition, we also characterize the discriminative co-clusters and propose three novel measures that can be used to evaluate the performance of any discriminative subspace pattern mining algorithm. We evaluated the proposed algorithms on several synthetic and real gene expression datasets, and our experimental results showed that the proposed algorithms outperformed several existing algorithms available in the literature.

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TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models

Cited by 42 publications

References 30 publications

Model‐based clustering and classification of functional data

Model‐based clustering and classification of functional data

Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering

Efficient mining of discriminative co-clusters from gene expression data

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