research-article

Pinball Loss Twin Support Vector Clustering

Authors: M. Tanveer, Tarun Gupta, Miten Shah, For the Alzheimer’s Disease Neuroimaging Initiative Authors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 2s

Article No.: 63, Pages 1 - 23

https://doi.org/10.1145/3409264

Published: 21 June 2021 Publication History

Abstract

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.

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Cited By

Wang HYu GMa J(2024)Fast sparse twin learning framework for large-scale pattern classificationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107730130(107730)Online publication date: Apr-2024
https://doi.org/10.1016/j.engappai.2023.107730
Zhu JChen SLiu YHu C(2024)Energy-based structural least squares twin support vector clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107467128:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107467
Liu YChen SZhu JHu C(2024)Plane-based clustering with asymmetric distribution lossApplied Soft Computing10.1016/j.asoc.2023.110893148:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.110893
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Index Terms

Pinball Loss Twin Support Vector Clustering
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 2s

June 2021

349 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3465440

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

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Association for Computing Machinery

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Publication History

Published: 21 June 2021

Accepted: 01 June 2020

Revised: 01 May 2020

Received: 01 February 2020

Published in TOMM Volume 17, Issue 2s

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Research-article
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Funding Sources

Science & Engineering Research Board (SERB) Government of INDIA under Ramanujan fellowship
Early Career Research Award (ECRA)
Council of Scientific & Industrial Research (CSIR), New Delhi, INDIA under Extra Mural Research (EMR) Scheme
Indian Institute of Technology Indore
Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health)
DOD ADNI (Department of Defense)
National Institute on Aging
National Institute of Biomedical Imaging and Bioengineering
AbbVie
Alzheimer’s Association
Alzheimer’s Drug Discovery Foundation
Araclon Biotech
BioClinica, Inc.
Bristol-Myers Squibb Company
CereSpir, Inc.
Cogstate
Eisai Inc.
Elan Pharmaceuticals, Inc.
Eli Lilly and Company
EuroImmun
F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.
Fujirebio
GE Healthcare
IXICO Ltd.
Janssen Alzheimer Immunotherapy Research & Development, LLC.
Johnson & Johnson Pharmaceutical Research & Development LLC.
Lumosity
Lundbeck
Merck & Co., Inc.
Meso Scale Diagnostics, LLC.
NeuroRx Research
Neurotrack Technologies
Novartis Pharmaceuticals Corporation
Pfizer Inc.
Piramal Imaging
Servier
Takeda Pharmaceutical Company
Transition Therapeutics
Canadian Institutes of Health Research
National Institutes of Health (www.fnih.org)

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Cited By

Wang HYu GMa J(2024)Fast sparse twin learning framework for large-scale pattern classificationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107730130(107730)Online publication date: Apr-2024
https://doi.org/10.1016/j.engappai.2023.107730
Zhu JChen SLiu YHu C(2024)Energy-based structural least squares twin support vector clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107467128:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107467
Liu YChen SZhu JHu C(2024)Plane-based clustering with asymmetric distribution lossApplied Soft Computing10.1016/j.asoc.2023.110893148:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.110893
Li DLi XGu FPan ZChen DMadden A(2023)A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory OptimizationSystems10.3390/systems1106031111:6(311)Online publication date: 19-Jun-2023
https://doi.org/10.3390/systems11060311
Damminsed VPanup WWangkeeree R(2023)Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised ClassificationIEEE Access10.1109/ACCESS.2023.326227011(31399-31416)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3262270
Li DGu FLi XDu RChen DMadden A(2023)Dynamic sales prediction with auto-learning and elastic-adjustment mechanism for inventory optimizationInformation Systems10.1016/j.is.2023.102259119(102259)Online publication date: Oct-2023
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Gu SChung FWang S(2023)Fuzzy style flat-based clusteringInformation Sciences: an International Journal10.1016/j.ins.2023.119321644:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119321
Malik AGao RGanaie MTanveer MSuganthan P(2023)Random vector functional link networkApplied Soft Computing10.1016/j.asoc.2023.110377143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110377
Xie XPu YZhang HMańdziuk JEl-Alfy EWang J(2023)An interpretable neural network for robustly determining the location and number of cluster centersInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01978-415:4(1473-1501)Online publication date: 21-Oct-2023
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Malik AGanaie MTanveer MSuganthan P(2023)Support Vector Machine Based Models with Sparse Auto-encoder Based Features for Classification ProblemNeural Information Processing10.1007/978-3-031-30105-6_21(248-259)Online publication date: 13-Apr-2023
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