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• W2257885648 abstract "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 an extension of the traditional latent class analysis model. 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." @default.
• W2257885648 created "2016-06-24" @default.
• W2257885648 creator A5049914126 @default.
• W2257885648 creator A5065970343 @default.
• W2257885648 date "2014-06-14" @default.
• W2257885648 modified "2023-09-27" @default.
• W2257885648 title "Model-based clustering of multivariate binary data with dimension reduction" @default.
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• W2257885648 hasPublicationYear "2014" @default.
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