HKUST Library Institutional Repository Banner

HKUST Institutional Repository >
Computer Science and Engineering >
CSE Technical Reports >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/783
Title: Learning hierarchical latent class models
Authors: Zhang, Nevin Lianwen
Kočka, Tomáš
Karciauskas, Gytis
Jensen, Finn V.
Keywords: Hierarchical latent class models
Cluster analysis
HLC models
Issue Date: 2003
Series/Report no.: Computer Science Technical Report ; HKUST-CS03-01
Abstract: Hierarchical latent class (HLC) models generalize latent class models. As models for cluster analysis, they suit more applications than the latter because they relax the often untrue conditional independence assumption. They also facilitate the discovery of latent causal structures and the induction of probabilistic models that capture complex dependencies and yet have low inferential complexity. In this paper, we investigate the problem of inducing HLC models from data. Two fundamental issues of general latent structure discovery are identified and methods to address those issues for HLC models are proposed. Based on the proposals, we develop an algorithm for learning HLC models and demonstrate the feasibility of learning HLC models that are large enough to be of practical interest.
URI: http://hdl.handle.net/1783.1/783
Appears in Collections:CSE Technical Reports

Files in This Item:

File Description SizeFormat
tr0301.pdf91KbAdobe PDFView/Open

All items in this Repository are protected by copyright, with all rights reserved.