{"title":"冗余样本和噪声环境下的新型鲁棒投影分布式广泛学习","authors":"Haoran Liu;Haiyang Pan;Jinde Zheng;Jinyu Tong;Jian Cheng","doi":"10.1109/TIM.2024.3460883","DOIUrl":null,"url":null,"abstract":"Broad learning system (BLS) is a breadth-based learning algorithm based on single-layer feedforward network (SLFN), which has the advantages of incremental learning with its fast-training speed and strong generalization ability. However, a large amount of redundant information is generated during feature mapping and enhancement in BLS. In addition, BLS has limited performance in eliminating the negative effects of discrete clustered points in noisy data. To address the above problems, a new robust projection distributed broad learning (RPDBL) is proposed in this article, which finds two projection directions in the feature space to achieve a good separation of projection samples and filter the noise and irrelevant information in the data so as to improve the robustness. Furthermore, to mitigate the problem of discrete clustered points, an additional regularization term is designed to ensure that the optimization problem is positive definite. The experimental results of rolling bearings show that compared with other breadth-based methods, RPDBL exhibits significant advantages in terms of accuracy, kappa, F-score, and precision.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Robust Projection Distributed Broad Learning Under Redundant Samples and Noisy Environment\",\"authors\":\"Haoran Liu;Haiyang Pan;Jinde Zheng;Jinyu Tong;Jian Cheng\",\"doi\":\"10.1109/TIM.2024.3460883\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Broad learning system (BLS) is a breadth-based learning algorithm based on single-layer feedforward network (SLFN), which has the advantages of incremental learning with its fast-training speed and strong generalization ability. However, a large amount of redundant information is generated during feature mapping and enhancement in BLS. In addition, BLS has limited performance in eliminating the negative effects of discrete clustered points in noisy data. To address the above problems, a new robust projection distributed broad learning (RPDBL) is proposed in this article, which finds two projection directions in the feature space to achieve a good separation of projection samples and filter the noise and irrelevant information in the data so as to improve the robustness. Furthermore, to mitigate the problem of discrete clustered points, an additional regularization term is designed to ensure that the optimization problem is positive definite. The experimental results of rolling bearings show that compared with other breadth-based methods, RPDBL exhibits significant advantages in terms of accuracy, kappa, F-score, and precision.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10680598/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10680598/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A New Robust Projection Distributed Broad Learning Under Redundant Samples and Noisy Environment
Broad learning system (BLS) is a breadth-based learning algorithm based on single-layer feedforward network (SLFN), which has the advantages of incremental learning with its fast-training speed and strong generalization ability. However, a large amount of redundant information is generated during feature mapping and enhancement in BLS. In addition, BLS has limited performance in eliminating the negative effects of discrete clustered points in noisy data. To address the above problems, a new robust projection distributed broad learning (RPDBL) is proposed in this article, which finds two projection directions in the feature space to achieve a good separation of projection samples and filter the noise and irrelevant information in the data so as to improve the robustness. Furthermore, to mitigate the problem of discrete clustered points, an additional regularization term is designed to ensure that the optimization problem is positive definite. The experimental results of rolling bearings show that compared with other breadth-based methods, RPDBL exhibits significant advantages in terms of accuracy, kappa, F-score, and precision.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.