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Breaking the curse of dimensionality: hierarchical Bayesian network model for multi-view clustering

Breaking the curse of dimensionality: hierarchical Bayesian network model for multi-view clustering Clustering high-dimensional data under the curse of dimensionality is an arduous task in many applications domains. The wide dimension yields the complexity-related challenges and the limited number of records leads to the overfitting trap. We propose to tackle this problematic using the graphical and probabilistic power of the Bayesian network. Our contribution is a new loose hierarchical Bayesian network model that encloses latent variables. These hidden variables are introduced for ensuring a multi-view clustering of the records. We propose a new framework for learning our proposed Bayesian network model. It starts by extracting the cliques of highly dependent features and it proceeds to learn representative latent variable for each features’ clique. The experimental results of our comparative analysis prove the efficiency of our model in tackling the distance concentration challenge. They also show the effectiveness of our model learning framework in skipping the overfitting trap, on benchmark high-dimensional datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Breaking the curse of dimensionality: hierarchical Bayesian network model for multi-view clustering

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/s10472-021-09749-z
Publisher site
See Article on Publisher Site

Abstract

Clustering high-dimensional data under the curse of dimensionality is an arduous task in many applications domains. The wide dimension yields the complexity-related challenges and the limited number of records leads to the overfitting trap. We propose to tackle this problematic using the graphical and probabilistic power of the Bayesian network. Our contribution is a new loose hierarchical Bayesian network model that encloses latent variables. These hidden variables are introduced for ensuring a multi-view clustering of the records. We propose a new framework for learning our proposed Bayesian network model. It starts by extracting the cliques of highly dependent features and it proceeds to learn representative latent variable for each features’ clique. The experimental results of our comparative analysis prove the efficiency of our model in tackling the distance concentration challenge. They also show the effectiveness of our model learning framework in skipping the overfitting trap, on benchmark high-dimensional datasets.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Nov 1, 2021

Keywords: Hierarchical Bayesian network; Multi-view clustering; Latent model; High-dimensional data

References