Clustering of multivariate binary data with dimension reduction via L1-regularized likelihood maximization

• 山本, 倫生

• Hayashi, Kenichi

抄録

Clustering methods with dimension reduction have been receiving considerable wide interest in statistics lately and a lot of methods to simultaneously perform clustering and dimension reduction have been proposed. This work presents a novel procedure for simultaneously determining the optimal cluster structure for multivariate binary data and the subspace to represent that cluster structure. The method is based on a finite mixture model of multivariate Bernoulli distributions, and each component is assumed to have a low-dimensional representation of the cluster structure. This method can be considered as an extension of the traditional latent class analysis. Sparsity is introduced to the loading values, which produces the low-dimensional subspace, for enhanced interpretability and more stable extraction of the subspace. An EM-based algorithm is developed to efficiently solve the proposed optimization problem. We demonstrate the effectiveness of the proposed method by applying it to a simulation study and real datasets.

収録刊行物

• Pattern Recognition

  Pattern Recognition 48 (12), 3959-3968, 2015-12

  Elsevier Ltd.

キーワード

• Binary data
• Clustering
• Dimension reduction
• EM algorithm
• Latent class analysis
• Sparsity

詳細情報

• CRID: 1050845760748971008
• NII論文ID: 120005690145
• NII書誌ID: AA00770025
• ISSN: 00313203
• DOI: 10.1016/j.patcog.2015.05.026
• HANDLE: 2433/202767
• Web Site: https://api.elsevier.com/content/article/PII:S0031320315002058?httpAccept=text/xml
• Web Site: https://api.elsevier.com/content/article/PII:S0031320315002058?httpAccept=text/plain
• 本文言語コード: en
• 資料種別: journal article
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