Applying discrete PCA in data analysis

作者：Wray Buntine, Aleks Jakulin · 发表于：Uncertainty in Artificial Intelligence · 年份：2004 · DOI：10.5555/1036843.1036851 · 被引用次数：110 · 研究领域：Blind Source Separation Techniques、Neural Networks and Applications、Algorithms and Data Compression

Methods for analysis of principal components in discrete data have existed for some time under various names such as grade of membership modelling, probabilistic latent semantic analysis, and genotype inference with admixture. In this paper we explore a number of extensions to the common theory, and present some application of these methods to some common statistical tasks. We show that these methods can be interpreted as a discrete version of ICA. We develop a hierarchical version yielding components at different levels of detail, and additional techniques for Gibbs sampling. We compare the algorithms on a text prediction task using support vector machines, and to information retrieval.