Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data

A co-clustering model for continuous data that relaxes the identically distributed assumption within blocks of traditional co-clustering is presented. The proposed model, although allowing more flexibility, still maintains the very high degree of parsimony achieved by traditional co-clustering. A stochastic EM algorithm along with a Gibbs sampler is used for parameter estimation and an ICL criterion is used for model selection. Simulated and real datasets are used for illustration and comparison with traditional co-clustering.

• Publication year

  2018

• Venue

  arXiv.org

• Publication date

  2018-08-25

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 1808.08366
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Mixtures of Probabilistic Principal Component Analysers

  2018cited by this paper
• Consistency and asymptotic normality of Latent Block Model estimators

  2017cited by this paper
• Model-based co-clustering for ordinal data

  2017cited by this paper
• Model-Based Clustering

  2016cited by this paper
• Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection

  2014cited by this paper
• Model-based clustering of high-dimensional data: A review

  2014influential reference
• A sparse variable selection procedure in model-based clustering

  2012cited by this paper
• Model-Based Co-clustering for Continuous Data

  2010cited by this paper
• Penalized model-based clustering with unconstrained covariance matrices.

  2009cited by this paper
• Parsimonious Gaussian mixture models

  2008cited by this paper
• Penalized Model-Based Clustering with Application to Variable Selection

  2007cited by this paper
• High-dimensional data clustering

  2006cited by this paper
• The EM Algorithm for Factor Analyzers:An Extension with Latent Variable

  2006cited by this paper
• Eigentaste: A Constant Time Collaborative Filtering Algorithm

  2001cited by this paper
• Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood

  2000cited by this paper
• Mixtures of Factor Analyzers

  2000cited by this paper
• Mixtures of Probabilistic Principal Component Analyzers

  1999cited by this paper
• Estimating the Dimension of a Model

  1978influential reference
• Direct Clustering of a Data Matrix

  1972cited by this paper
• Clustering methods based on likelihood ratio criteria.

  1971cited by this paper
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains

  1970cited by this paper
• A COMPUTER PROGRAM FOR THE MAXIMUM LIKELIHOOD ANALYSIS OF TYPES

  1965cited by this paper

• Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers

  2019influential citation