ePrints.FRI - University of Ljubljana, Faculty of Computer and Information Science

Applying Discrete PCA in Data Analysis

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..

Download (277Kb)


    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:
    URLURL Type
    http://www.hiit.fi/u/buntine/uai2004buntine.pdfAlternative location
    http://eprints.pascal-network.org/archive/00000143/01/final.psAlternative location
    http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(id=4360788)Alternative location
    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
    URI: http://eprints.fri.uni-lj.si/id/eprint/144

    Actions (login required)

    View Item