Machine Intelligence and Networked Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 1840

Special Issue Editors

Automation Department, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
Interests: neural networks; machine learning; information fusion; deep learning
Special Issues, Collections and Topics in MDPI journals
Automation Department, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
Interests: control theory; fuzzy systems; complex systems; robot control systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of algorithms, frameworks, hardware, and the increased volume of data has increased the popularity of machine intelligence algorithms for the purpose of solving problems in networked systems. Machine intelligence-based techniques and emerging new computing paradigms could extract sophisticated features and help us more easily address the practical problems that occur in networked systems.

This Special Issue aims to highlight and present the latest developments in machine intelligence and networked systems, addressing the challenges of the application of advanced machine intelligence algorithms, frameworks, and technologies to networked systems. Research fields could include industry, traffic, energy, biology, economy, environment, management, etc. Both theoretical and experimental contributions containing novel applications with new insights and findings in the field of networked systems and machine intelligence are welcome.

Prof. Dr. Haitao Zhao
Dr. Meng Wang
Guest Editors

Manuscript Submission Information

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Keywords

  • machine intelligence
  • networked systems
  • neural networks
  • complex systems
  • fuzzy systems
  • robot control systems
  • machine learning
  • computer vision
  • information fusion
  • deep learning

Published Papers (1 paper)

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Research

18 pages, 2530 KiB  
Article
Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
by Zonghan Shi and Haitao Zhao
Appl. Sci. 2023, 13(15), 8791; https://doi.org/10.3390/app13158791 - 29 Jul 2023
Cited by 2 | Viewed by 1022
Abstract
Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of [...] Read more.
Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of the self-expressive matrix increases quadratically with the number of input data points, making it difficult to handle large-scale datasets. Moreover, since multiple views are rich in information, both consistency and specificity of the input images need to be considered. To solve these problems, we propose a novel deep multi-view clustering approach based on the reconstructed self-expressive matrix (DCRSM). We use a reconstruction module to approximate self-expressive coefficients using only a small number of training samples, while the conventional self-expressive model must train the network with entire datasets. We also use shared layers and specific layers to integrate consistent and specific information of features to fuse information between views. The proposed DCRSM is extensively evaluated on multiple datasets, including Fashion-MNIST, COIL-20, COIL-100, and YTF. The experimental results demonstrate its superiority over several existing multi-view clustering methods, achieving an improvement between 1.94% and 4.2% in accuracy and a maximum improvement of 4.5% in NMI across different datasets. Our DCRSM also yields competitive results even when trained by 50% samples of the whole datasets. Full article
(This article belongs to the Special Issue Machine Intelligence and Networked Systems)
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