Wray Buntine and Aleks Jakulin (2004) Applying Discrete PCA in Data Analysis. In: 20th Conference on Uncertainty in Artificial Intelligence (UAI), July, 2004, Banff, Alberta, Canada..
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.
|Item Type: ||Conference or Workshop Item (Paper)|
|Keywords: ||multinomial principal component analysis, independent component analysis, Gibbs sampling, discrete data, text mining, constructive induction|
|Language of Content: ||English|
|Related URLs: |
|Institution: ||University of Ljubljana|
|Department: ||Faculty of Computer and Information Science|
|Divisions: ||Faculty of Computer and Information Science > Artificial Intelligence Laboratory|
|Item ID: ||144|
|Date Deposited: ||07 Oct 2004|
|Last Modified: ||06 Dec 2013 11:49|
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