Abstract
The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was made earlier by Zhang (2002), but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.
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*The research is supported by Hong Kong Grants Council Grants #622105 and #622307, and the National Basic Research Program of China (aka the 973 Program) under project No. 2003CB517106.
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ZHANG, N.L., WANG, Y. & CHEN, T. Discovery of latent structures: Experience with the CoIL Challenge 2000 data set*. J Syst Sci Complex 21, 172–183 (2008). https://doi.org/10.1007/s11424-008-9101-2
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DOI: https://doi.org/10.1007/s11424-008-9101-2