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Simultaneous Bayesian clustering and feature selection through student’s t mixtures model

Simultaneous Bayesian clustering and feature selection through student’s t mixtures model

Sun, Jianyong, Zhou, Aimin, Keates, Simeon ORCID: 0000-0002-2826-672X and Liao, Shengbin (2017) Simultaneous Bayesian clustering and feature selection through student’s t mixtures model. In: IEEE Transactions on Neural Networks and Learning Systems. IEEE, pp. 1-13. ISSN 2162-237X (Print), 2162-2388 (Online) (doi:https://doi.org/10.1109/TNNLS.2016.2619061)

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Abstract

In this paper, we proposed a generative model for feature selection under the unsupervised learning context. The model assumes that data are independently and identically sampled from a finite mixture of Student’s t distributions, which can reduce the sensitiveness to outliers. Latent random variables that represent the features’ salience are included in the model for the indication of the relevance of features. As a result, the model is expected to simultaneously realize clustering, feature selection, and outlier detection. Inference is carried out by a tree-structured variational Bayes algorithm. Full Bayesian treatment is adopted in the model to realize automatic model selection. Controlled experimental studies showed that the developed model is capable of modeling the data set with outliers accurately. Further- more, experiment results showed that the developed algorithm compares favorably against existing unsupervised probability model-based Bayesian feature selection algorithms on artificial and real data sets. Moreover, the application of the developed algorithm on real leukemia gene expression data indicated that it is able to identify the discriminating genes successfully.

Item Type: Conference Proceedings
Title of Proceedings: IEEE Transactions on Neural Networks and Learning Systems
Uncontrolled Keywords: Bayesian inference, Feature selection, Robust clustering, Tree-structured variational Bayes (VB)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Last Modified: 30 Apr 2018 14:28
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT a
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/17655

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