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Simultaneous feature selection and clustering using mixture models.

Martin H C Law1, Mário A T Figueiredo, Anil K Jain

  • 1Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan 48824-1226, USA. lawhiu@cse.msu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 4, 2005
PubMed
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This study introduces feature saliency for unsupervised learning, enabling automatic feature selection in clustering. An expectation-maximization algorithm refines feature relevance and determines the optimal number of clusters.

Area of Science:

  • Data Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Clustering is a key unsupervised learning method for identifying data structures.
  • Feature selection is crucial but challenging in clustering due to the absence of labels.
  • Determining the number of clusters is interdependent with feature selection.

Purpose of the Study:

  • To propose and develop a method for feature selection within clustering.
  • To introduce the concept of feature saliency for guiding clustering algorithms.
  • To address the challenge of simultaneously determining feature relevance and the number of clusters.

Main Methods:

  • Developed an expectation-maximization (EM) algorithm to estimate feature saliency.
  • Integrated a minimum message length (MML) model selection criterion.

Related Experiment Videos

  • Extended the EM algorithm and MML criterion for joint estimation of feature saliencies and cluster numbers.

    • Main Results:

    • The proposed method effectively drives the saliency of irrelevant features towards zero, performing automatic feature selection.
    • The expectation-maximization algorithm successfully estimates feature saliencies in mixture-based clustering.
    • The extended approach enables simultaneous determination of feature importance and the optimal number of clusters.

    Conclusions:

    • Feature saliency offers a novel approach to feature selection in unsupervised clustering.
    • The developed EM algorithm and MML criterion provide a robust framework for this task.
    • This work enhances clustering by integrating feature selection and cluster number determination.