期刊名称：The World of Computer Science and Information Technology Journal
印刷版ISSN：2221-0741
出版年度：2014
卷号：4
期号：8
页码：5
语种：English
出版社：WCSIT Publishing
摘要：modeling data heterogeneity by a mixture of local models and exploiting the correlation in the localized data subsets to reduce their subspace dimensionalities has been realized in many mixture models; like PCA mixture and FA mixture models. Determining the number of local models as well as the proper dimensionality for each subspace (local model space) are the most difficult questions of these models. Instead of using fixed ad-hoc dimensionality for all local models, this paper proposes using a global preserved variance percentage value to estimate the dimensionality that retains the given variability percentage in each subspace. We test the proposed method on classifying handwritten digit by a mixture of Probabilistic PCA model, the result shows that the proposed method outperforms fixed dimensionality probabilistic PCA mixture model.
