Quadratically gated mixture of experts for incomplete data classification

Xuejun Liao, Hui Li, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations


We introduce quadratically gated mixture of experts (QGME), a statistical model for multi-class nonlinear classification. The QGME is formulated in the setting of incomplete data, where the data values are partially observed. We show that the missing values entail joint estimation of the data manifold and the classifier, which allows adaptive imputation during classifier learning. The expectation maximization (EM) algorithm is derived for joint likelihood maximization, with adaptive imputation performed analytically in the E-step. The performance of QGME is evaluated on three benchmark data sets and the results show that the QGME yields significant improvements over competing methods.
Original languageEnglish (US)
Title of host publicationACM International Conference Proceeding Series
Number of pages8
StatePublished - Aug 23 2007
Externally publishedYes


Dive into the research topics of 'Quadratically gated mixture of experts for incomplete data classification'. Together they form a unique fingerprint.

Cite this