{"title":"基于ica的逆FDA FKP识别","authors":"Zhongxi Sun","doi":"10.1109/YAC.2018.8406376","DOIUrl":null,"url":null,"abstract":"ICA concerns high-order dependencies between variables. In this paper, a new feature extraction method is proposed by combining Inverse FDA with ICA. ICA is applied to sample images to provide the high-order statistical information and reduce dimension. Inverse FDA is used for discrimination. Experimental results on FKP database show that our proposed method is efficient.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"560 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"FKP recognition using ICA-based inverse FDA\",\"authors\":\"Zhongxi Sun\",\"doi\":\"10.1109/YAC.2018.8406376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ICA concerns high-order dependencies between variables. In this paper, a new feature extraction method is proposed by combining Inverse FDA with ICA. ICA is applied to sample images to provide the high-order statistical information and reduce dimension. Inverse FDA is used for discrimination. Experimental results on FKP database show that our proposed method is efficient.\",\"PeriodicalId\":226586,\"journal\":{\"name\":\"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)\",\"volume\":\"560 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/YAC.2018.8406376\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YAC.2018.8406376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ICA concerns high-order dependencies between variables. In this paper, a new feature extraction method is proposed by combining Inverse FDA with ICA. ICA is applied to sample images to provide the high-order statistical information and reduce dimension. Inverse FDA is used for discrimination. Experimental results on FKP database show that our proposed method is efficient.