Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11424-008-9101-2 | J. Systems Science & Complexity |
Keywords | Field | DocType |
learning.,bayesian networks,latent structure discovery,case study,latent variable,bayesian network | Computer science,Latent class model,Latent variable,Electromagnetic coil,Probabilistic latent semantic analysis,Artificial intelligence,Machine learning,Zhàng | Journal |
Volume | Issue | ISSN |
21 | 2 | 1559-7067 |
Citations | PageRank | References |
5 | 0.47 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nevin .L Zhang | 1 | 895 | 97.21 |
| Yi WANG | 2 | 28 | 2.08 |
| Tao Chen | 3 | 76 | 7.04 |