Simultaneous high-dimensional clustering and feature selection using asymmetric Gaussian mixture models

作者：

Highlights：

• We introduce the multidimensional asymmetric Gaussian mixture (AGGM).

• We propose two novel inference frameworks for unsupervised non-Gaussian feature selection.

• We have used these frameworks for challenging applications.

摘要

•We introduce the multidimensional asymmetric Gaussian mixture (AGGM).•We propose two novel inference frameworks for unsupervised non-Gaussian feature selection.•We have used these frameworks for challenging applications.

论文关键词：Asymmetric Gaussian distribution,Mixture modeling,Expectation-maximization (EM),Rival penalized EM (RPEM),Feature selection,Model selection,Minimum message length (MML),Scene categorization,Facial expression recognition

论文评审过程：Received 9 February 2014, Revised 28 August 2014, Accepted 31 October 2014, Available online 3 December 2014.

论文官网地址：https://doi.org/10.1016/j.imavis.2014.10.011