Name
Abstract | ||
|---|---|---|
In the general classification context the recourse to the so-called Bayes decision rule requires to estimate the class conditional probability density functions. A mixture model for the observed variables which is derived by assuming that the data have been generated by an independent factor model is proposed. Independent factor analysis is in fact a generative latent variable model whose structure closely resembles the one of the ordinary factor model, but it assumes that the latent variables are mutually independent and not necessarily Gaussian. The method therefore provides a dimension reduction together with a semiparametric estimate of the class conditional probability density functions. This density approximation is plugged into the classic Bayes rule and its performance is evaluated both on real and simulated data. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.csda.2007.09.026 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
independent factor analysis,variable model,classification,ordinary factor model,latent variable,independent factor model,density approximation,mixture model,classic bayes rule,mixture models.,independent factor discriminant analysis,generative latent,class conditional probability density,decision rule,discriminant analysis,latent variable model,conditional probability,factor model,mixture models,dimension reduction,bayes rule | Density estimation,Econometrics,Conditional probability distribution,Conditional probability,Latent variable model,Latent class model,Statistics,Independence (probability theory),Mathematics,Bayes' theorem,Generative model | Journal |
Volume | Issue | ISSN |
52 | 6 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
6 | 0.56 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angela Montanari | 1 | 35 | 5.01 |
| Daniela G. Calò | 2 | 24 | 3.63 |
| Cinzia Viroli | 3 | 51 | 7.94 |