Quadratically gated mixture of experts for incomplete data classification

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.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Xuejun Liao, Hui Li, Lawrence Carin
• Keywords: Adaptive Imputation, Benchmark Data Sets, ICML 2007, Joint Likelihood Maximization, Machine Learning