{"title":"基于最大相关熵准则的图信号估计(A. Chandrasekar的工作得到了materials, SERB, India的支持，Under Grant MTR/2021/000405)","authors":"A. Chandrasekar, S. Radhika","doi":"10.1109/ICECONF57129.2023.10084250","DOIUrl":null,"url":null,"abstract":"In order to estimate the graph signal from the small part of samples, a novel adaptive filtering method based on maximum correntropy criterion is proposed in this study. The proposed approach is resistant to environments with impulsive noise. The performance enhancement of the proposed technique is well demonstrated by the simulation results carried out in the context of weather data.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Graph Signal Estimation Based on Maximum Correntropy Criterion (The Work of A. Chandrasekar Was Supported by the MATRICS, SERB, India, Under Grant MTR/2021/000405)\",\"authors\":\"A. Chandrasekar, S. Radhika\",\"doi\":\"10.1109/ICECONF57129.2023.10084250\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to estimate the graph signal from the small part of samples, a novel adaptive filtering method based on maximum correntropy criterion is proposed in this study. The proposed approach is resistant to environments with impulsive noise. The performance enhancement of the proposed technique is well demonstrated by the simulation results carried out in the context of weather data.\",\"PeriodicalId\":436733,\"journal\":{\"name\":\"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECONF57129.2023.10084250\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECONF57129.2023.10084250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graph Signal Estimation Based on Maximum Correntropy Criterion (The Work of A. Chandrasekar Was Supported by the MATRICS, SERB, India, Under Grant MTR/2021/000405)
In order to estimate the graph signal from the small part of samples, a novel adaptive filtering method based on maximum correntropy criterion is proposed in this study. The proposed approach is resistant to environments with impulsive noise. The performance enhancement of the proposed technique is well demonstrated by the simulation results carried out in the context of weather data.
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