Robust Ranking Kernel Support Vector Machine via Manifold Regularized Matrix Factorization for Multi-Label Classification
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Published:2024-01-11
Issue:2
Volume:14
Page:638
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Song Heping12ORCID, Zhou Yiming1, Quayson Ebenezer13, Zhu Qian1, Shen Xiangjun1
Affiliation:
1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China 2. Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications, Zhenjiang 212013, China 3. Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani 00233, Ghana
Abstract
Multi-label classification has been extensively researched and utilized for several decades. However, the performance of these methods is highly susceptible to the presence of noisy data samples, resulting in a significant decrease in accuracy when noise levels are high. To address this issue, we propose a robust ranking support vector machine (Rank-SVM) method that incorporates manifold regularized matrix factorization. Unlike traditional Rank-SVM methods, our approach integrates feature selection and multi-label learning into a unified framework. Within this framework, we employ matrix factorization to learn a low-rank robust subspace within the input space, thereby enhancing the robustness of data representation in high-noise conditions. Additionally, we incorporate manifold structure regularization into the framework to preserve manifold relationships among low-rank samples, which further improves the robustness of the low-rank representation. Leveraging on this robust low-rank representation, we extract a resilient low-rank features and employ them to construct a more effective classifier. Finally, the proposed framework is extended to derive a kernelized ranking approach, for the creation of nonlinear multi-label classifiers. To effectively solve this non-convex kernelized method, we employ the augmented Lagrangian multiplier (ALM) and alternating direction method of multipliers (ADMM) techniques to obtain the optimal solution. Experimental evaluations conducted on various datasets demonstrate that our framework achieves superior classification results and significantly enhances performance in high-noise scenarios.
Funder
National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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