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ファイル | 記述 | サイズ | フォーマット | |
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j.patcog.2015.05.026.pdf | 382.93 kB | Adobe PDF | 見る/開く |
タイトル: | Clustering of multivariate binary data with dimension reduction via L1-regularized likelihood maximization |
著者: | Yamamoto, Michio Hayashi, Kenichi |
著者名の別形: | 山本, 倫生 |
キーワード: | Binary data Clustering Dimension reduction EM algorithm Latent class analysis Sparsity |
発行日: | Dec-2015 |
出版者: | Elsevier Ltd. |
誌名: | Pattern Recognition |
巻: | 48 |
号: | 12 |
開始ページ: | 3959 |
終了ページ: | 3968 |
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. |
著作権等: | © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ The full-text file will be made open to the public on 1 Descember 2017 in accordance with publisher's 'Terms and Conditions for Self-Archiving'. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 This is not the published version. Please cite only the published version. |
URI: | http://hdl.handle.net/2433/202767 |
DOI(出版社版): | 10.1016/j.patcog.2015.05.026 |
出現コレクション: | 学術雑誌掲載論文等 |
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