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

Title:

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

Fan, Wentao, Bouguila, Nizar and Ziou, Djemel (2013) Unsupervised Hybrid Feature Extraction Selection for High-Dimensional Non-Gaussian Data Clustering with Variational Inference. IEEE Transactions on Knowledge and Data Engineering, 25 (7). pp. 1670-1685. ISSN 1041-4347

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Official URL: http://dx.doi.org/10.1109/TKDE.2012.101

Abstract

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.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Article
Refereed:Yes
Authors:Fan, Wentao and Bouguila, Nizar and Ziou, Djemel
Journal or Publication:IEEE Transactions on Knowledge and Data Engineering
Date:July 2013
Digital Object Identifier (DOI):10.1109/TKDE.2012.101
Keywords:Bayesian estimation, Mixture models, feature selection, generalized Dirichlet, human action videos, model selection, unsupervised learning, variational inference
ID Code:977855
Deposited By: DANIELLE DENNIE
Deposited On:27 Sep 2013 14:23
Last Modified:18 Jan 2018 17:45
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