کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
532007 | 869897 | 2006 | 11 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: On fast supervised learning for normal mixture models with missing information On fast supervised learning for normal mixture models with missing information](/preview/png/532007.png)
It is an important research issue to deal with mixture models when missing values occur in the data. In this paper, computational strategies using auxiliary indicator matrices are introduced for efficiently handling mixtures of multivariate normal distributions when the data are missing at random and have an arbitrary missing data pattern, meaning that missing data can occur anywhere. We develop a novel EM algorithm that can dramatically save computation time and be exploited in many applications, such as density estimation, supervised clustering and prediction of missing values. In the aspect of multiple imputations for missing data, we also offer a data augmentation scheme using the Gibbs sampler. Our proposed methodologies are illustrated through some real data sets with varying proportions of missing values.
Journal: Pattern Recognition - Volume 39, Issue 6, June 2006, Pages 1177–1187