A LASSO-penalized BIC for mixture model selection

The efficacy of family-based approaches to mixture model-based clustering and classification depends on the selection of parsimonious models. Current wisdom suggests the Bayesian information criterion (BIC) for mixture model selection. However, the BIC has well-known limitations, including a tendency to overestimate the number of components as well as a proclivity for underestimating, often drastically, the number of components in higher dimensions. While the former problem might be soluble by merging components, the latter is impossible to mitigate in clustering and classification applications. In this paper, a LASSO-penalized BIC (LPBIC) is introduced to overcome this problem. This approach is illustrated based on applications of extensions of mixtures of factor analyzers, where the LPBIC is used to select both the number of components and the number of latent factors. The LPBIC is shown to match or outperform the BIC in several situations.

• Publication year

  2012

• Venue

  Advances in Data Analysis and Classification

• Publication date

  2012-11-27

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/s11634-013-0155-1arXiv 1211.6451
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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