Unsupervised Hybrid Feature Extraction Selection for High-Dimensional Non-Gaussian Data Clustering with Variational Inference

作者：Fan Wentao^*; Bouguila Nizar; Ziou Djemel

来源：IEEE Transactions on Knowledge and Data Engineering, 2013, 25(7): 1670-1685.

DOI：10.1109/TKDE.2012.101

• Mixture models
• unsupervised learning
• generalized Dirichlet
• model selection
• feature selection
• Bayesian estimation
• variational inference
• human action videos

Clustering has been a subject of extensive research in data mining, pattern recognition, and other areas for several decades. The main goal is to assign samples, which are typically non-Gaussian and expressed as points in high-dimensional feature spaces, to one of a number of clusters. It is well known that in such high-dimensional settings, the existence of irrelevant features generally compromises modeling capabilities. In this paper, we propose a variational inference framework for unsupervised non-Gaussian feature selection, in the context of finite generalized Dirichlet (GD) mixture-based clustering. Under the proposed principled variational framework, we simultaneously estimate, in a closed form, all the involved parameters and determine the complexity (i.e., both model an feature selection) of the GD mixture. Extensive simulations using synthetic data along with an analysis of real-world data and human action videos demonstrate that our variational approach achieves better results than comparable techniques.

• 出版日期2013-7