Sciweavers

Share
ICCS
2007
Springer

Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set

10 years 3 months ago
Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set
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 [6], 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.
Nevin Lianwen Zhang
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where ICCS
Authors Nevin Lianwen Zhang
Comments (0)
books