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Mathematics
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Optimization Models and Algorithms in Data Science
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Optimization Models and Algorithms in Data Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 3077

Special Issue Editors

Dr. Ming Yang

E-Mail Website
Guest Editor
College of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China
Interests: tensors; low rank model; multi-view clustering; signal processing; image processing; sparse coding; machine learning; data science
Dr. Liqun Shan

E-Mail Website
Guest Editor
1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70503, USA
2. School of Physical and Electrical Engineering, Northeast Petroleum University, Daqing 163318, China
Interests: data mining with cross-domain data; unconventional oil and gas reservoir development; machine learning; computer vision

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the optimization and applications of models and algorithms in data science. The papers in this Special Issue cover various aspects of data science, including novel algorithms and models, theoretical analysis, and applications in real-world problems. Some of the topics covered include tensor decomposition, tensor robust principal component analysis, tensor completion, low-rank models, multi-view clustering and sparse coding. Tensor decomposition is a powerful tool for modeling high-dimensional data and has applications in a wide range of fields, including image processing, signal processing, and machine learning. The papers also showcase the latest developments in low-rank models and their application in data science.

Dr. Ming Yang
Dr. Liqun Shan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tensor decomposition
  • low-rank models
  • multi-view clustering
  • sparse coding
  • tensor completion
  • tensor robust principal component analysis
  • machine learning
  • data science
  • signal processing
  • image processing
  • high-dimensional data

Published Papers (3 papers)

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Research

12 pages, 972 KiB  
Article
Regularized Discrete Optimal Transport for Class-Imbalanced Classifications
by Jiqiang Chen, Jie Wan and Litao Ma
Mathematics 2024, 12(4), 524; https://doi.org/10.3390/math12040524 - 07 Feb 2024
Viewed by 555
Abstract
Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that [...] Read more.
Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that leverages optimal transport for handling imbalanced data. Specifically, we establish a transport plan between training and testing data without modifying the original data distribution, drawing upon the principles of optimal transport theory. Additionally, we introduce a non-convex interclass regularization term to establish connections between testing samples and training samples with the same class labels. This regularization term forms the basis of a regularized discrete optimal transport model, which is employed to address imbalanced classification scenarios. Subsequently, in line with the concept of maximum minimization, a maximum minimization algorithm is introduced for regularized discrete optimal transport. Subsequent experiments on 17 Keel datasets with varying levels of imbalance demonstrate the superior performance of the proposed approach compared to 11 other widely used techniques for class-imbalanced classification. Additionally, the application of the proposed approach to water quality evaluation confirms its effectiveness. Full article
(This article belongs to the Special Issue Optimization Models and Algorithms in Data Science)
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20 pages, 2711 KiB  
Article
A Novel Memory Concurrent Editing Model for Large-Scale Video Streams in Edge Computing
by Haitao Liu, Qingkui Chen and Puchen Liu
Mathematics 2023, 11(14), 3175; https://doi.org/10.3390/math11143175 - 19 Jul 2023
Viewed by 663
Abstract
Efficient management and utilization of edge server memory buffers are crucial for improving the efficiency of concurrent editing in the concurrent editing application scenario of large-scale video in edge computing. In order to elevate the efficiency of concurrent editing and the satisfaction of [...] Read more.
Efficient management and utilization of edge server memory buffers are crucial for improving the efficiency of concurrent editing in the concurrent editing application scenario of large-scale video in edge computing. In order to elevate the efficiency of concurrent editing and the satisfaction of service users under the constraint of limited memory buffer resources, the allocation of memory buffers of concurrent editing servers is transformed into the bin-packing problem, which is solved using an ant colony algorithm to achieve the least loaded utilization batch. Meanwhile, a new distributed online concurrent editing algorithm for video streams is designed for the conflict problem of large-scale video editing in an edge computing environment. It incorporates dual-buffer read-and-write technology to solve the difficult problem of concurrent inefficiency of editing and writing disks. The experimental results of the simulation show that the scheme not only achieves a good performance in the scheduling of concurrent editing but also implements the editing resource allocation function in an efficient and reasonable way. Compared with the benchmark traditional single-exclusive editing scheme, the proposed optimized scheme can simultaneously enhance editing efficiency and user satisfaction under the restriction of providing the same memory buffer computing resources. The proposed model has a wide application to video real-time processing application scenarios in edge computing. Full article
(This article belongs to the Special Issue Optimization Models and Algorithms in Data Science)
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20 pages, 5406 KiB  
Article
An Optimization Method of Large-Scale Video Stream Concurrent Transmission for Edge Computing
by Haitao Liu, Qingkui Chen and Puchen Liu
Mathematics 2023, 11(12), 2622; https://doi.org/10.3390/math11122622 - 08 Jun 2023
Cited by 1 | Viewed by 1323
Abstract
Concurrent access to large-scale video data streams in edge computing is an important application scenario that currently faces a high cost of network access equipment and high data packet loss rate. To solve this problem, a low-cost link aggregation video stream data concurrent [...] Read more.
Concurrent access to large-scale video data streams in edge computing is an important application scenario that currently faces a high cost of network access equipment and high data packet loss rate. To solve this problem, a low-cost link aggregation video stream data concurrent transmission method is proposed. Data Plane Development Kit (DPDK) technology supports the concurrent receiving and forwarding function of multiple Network Interface Cards (NICs). The Q-learning data stream scheduling model is proposed to solve the load scheduling of multiple queues of multiple NICs. The Central Processing Unit (CPU) transmission processing unit was dynamically selected by data stream classification, as well as a reward function, to achieve the dynamic load balancing of data stream transmission. The experiments conducted demonstrate that this method expands the bandwidth by 3.6 times over the benchmark scheme for a single network port, and reduces the average CPU load ratio by 18%. Compared to the UDP and DPDK schemes, it lowers the average system latency by 21%, reduces the data transmission packet loss rate by 0.48%, and improves the overall system transmission throughput. This transmission optimization scheme can be applied in data centers and edge computing clusters to improve the communication performance of big data processing. Full article
(This article belongs to the Special Issue Optimization Models and Algorithms in Data Science)
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