A Deep Fusion Gaussian Mixture Model for Multiview Land Data Clustering

Peng Li,Lu Wang,Shan Jin,Feng Xia,Wenhan Zhao,Jing Gao,Jianing Zhang,Zhikui Chen

doi:10.1155/2020/8880430

Abstract

With the rapid industrialization and urbanization, pattern mining of soil contamination of heavy metals is attracting increasing attention to control soil contamination. However, the correlation over various heavy metals and the high-dimension representation of heavy metal data pose vast challenges on the accurate mining of patterns over heavy metals of soil contamination. To solve those challenges, a multiview Gaussian mixture model is proposed in this paper, to naturally capture complicated relationships over multiviews on the basis of deep fusion features of data. Specifically, a deep fusion feature architecture containing modality-specific and modality-common stacked autoencoders is designed to distill fusion representations from the information of all views. Then, the Gaussian mixture model is extended on the fusion representations to naturally recognize the accurate patterns of the intra- and inter-views. Finally, extensive experiments are conducted on the representative datasets to evaluate the performance of the multiview Gaussian mixture model. Results show the outperformance of the proposed methods.

Highlights

With the rapid industrialization and urbanization over the world, environmental contamination is attracting increasing attention nowadays, which is caused by unreasonable usage of natural resources, such as the overuse of coal [1]
The Gaussian mixture model is extended on the fusion representations to naturally recognize the accurate patterns of the intra- and inter-views
K-means-DM and Gaussian mixture model (GMM)-DM denote the K-means and GMM performed on the deep representations of MNIST, which is extracted by the modality-specific stacked autoencoder

Summary

Introduction

With the rapid industrialization and urbanization over the world, environmental contamination is attracting increasing attention nowadays, which is caused by unreasonable usage of natural resources, such as the overuse of coal [1]. A large number of researchers force on the control of soil contamination of heavy metals by mining intrinsic patterns hidden over various heavy metals which can do a favor to the contamination control and environmental protection. The correlation over various heavy metals and the highdimension representation of heavy metal data pose vast challenges on the accurate mining of patterns over heavy metals of soil contamination. With the continuous development of industrialization and urbanization, more research is still required to capture effective patterns of highdimension representation of heavy metal data, to control the soil contamination. Chen et al used multivariate statistics and geostatistics to explore distributions of heavy metals in the soil of northwest China, which can capture pollution sources of heavy metals based on patterns of distributions [9]

Methods

Results

Conclusion