Abstract
We 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 ?, but that study involved only small applications with 4 or 5 observed variables. Due to recent progress in algorithm research, it is now possible to learn HLC models with dozens of observed variables. We have successfully analyzed a version the CoIL Challenge 2000 data set that consists of 42 observed variable. The model obtained consists of 22 latent variables, and its structure is intuitively appealing.
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© 2007 Springer Berlin Heidelberg
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Zhang, N.L. (2007). Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72590-9_4
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DOI: https://doi.org/10.1007/978-3-540-72590-9_4
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