Finding uninformative features in binary data

Xu Wang, Ata Kaban

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


For statistical modelling of multivariate binary data, such as text documents, datum instances are typically represented as vectors over a global vocabulary of attributes. Apart from the issue of high dimensionality, this also faces us with the problem of uneven importance of various attribute presences/absences. This problem has been largely overlooked in the literature, however it may create difficulties in obtaining reliable estimates of unsupervised probabilistic representation models. In turn, the problem of automated feature selection and feature weighting in the context of unsupervised learning is challenging, because there is no known target to guide the search. In this paper we propose and study a relatively simple cluster-based generative model for multivariate binary data, equipped with automated feature weighting capability. Empirical results on both synthetic and real data sets are given and discussed.
Original languageEnglish
Pages (from-to)40-47
Number of pages8
JournalLecture Notes in Computer Science
Publication statusPublished - 1 Jan 2005
Event6th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005), Jul 06-08, 2005. Brisbane, Australia -
Duration: 1 Jan 2005 → …


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