Quadratically Gated Mixture of Exp erts for Incomplete Data Classification

Quadratically Gated Mixture of Exp erts for Incomplete Data Classification
Xuejun Liao - Duke University, USA Hui Li - Duke University, USA Lawrence Carin - Duke University, USA
We introduce quadratical ly 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.

Xuejun Liao - Duke University, USA
Hui Li - Duke University, USA
Lawrence Carin - Duke University, USA

We introduce quadratical ly 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.