Semi-supervised logistic discrimination via regularized gaussian basis expansions

Shuichi Kawano, Sadanori Konishi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The problem of constructing classification methods based on both labeled and unlabeled data sets is considered for analyzing data with complex structures. We introduce a semi-supervised logistic discriminant model with Gaussian basis expansions. Unknown parameters included in the logistic model are estimated by regularization method along with the technique of EM algorithm. For selection of adjusted parameters, we derive a model selection criterion from Bayesian viewpoints. Numerical studies are conducted to investigate the effectiveness of our proposed modeling procedures.

Original languageEnglish
Pages (from-to)2412-2423
Number of pages12
JournalCommunications in Statistics - Theory and Methods
Volume40
Issue number13
DOIs
Publication statusPublished - Jan 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Fingerprint

Dive into the research topics of 'Semi-supervised logistic discrimination via regularized gaussian basis expansions'. Together they form a unique fingerprint.

Cite this