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The authors present a case study to demonstrate the possibility of discovering complexand interesting latent structures using hierarchical latent class (HLC) models.A similar effort wasmade earlier by Zhang (2002),but that study involved only small applications with 4 or 5 observedvariables and no more than 2 latent variables due to the lack of efficient learning algorithms.Significantprogress has been made since then on algorithmic research,and it is now possible to learn HLC modelswith dozens of observed variables.This allows us to demonstrate the benefits of HLC models moreconvincingly than before.The authors have successfully analyzed the CoIL Challenge 2000 data setusing HLC models.The model obtained consists of 22 latent variables,and its structure is intuitivelyappealing.It is exciting to know that such a large and meaningful latent structure can be automaticallyinferred from data.
The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent classes (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 .Significantprogress has been made since then on algorithmic research, and it is now possible to learn HLC modelswith dozens of observed variables. This allows us to demonstrate the benefits of HLC models moreconvincingly 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 intuitivelyappealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.