{"title":"彩色图像分类的稳定三阶张量表示","authors":"D. Tao, S. Maybank, Weiming Hu, Xuelong Li","doi":"10.1109/WI.2005.136","DOIUrl":null,"url":null,"abstract":"General tensors can represent colour images more naturally than conventional features; however, the general tensors' stability properties are not reported and remain to be a key problem. In this paper, we use the tensor minimax probability (TMPM) to prove that the tensor representation is stable. The proof is based on the random subspace method through a large number of experiments.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Stable third-order tensor representation for colour image classification\",\"authors\":\"D. Tao, S. Maybank, Weiming Hu, Xuelong Li\",\"doi\":\"10.1109/WI.2005.136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"General tensors can represent colour images more naturally than conventional features; however, the general tensors' stability properties are not reported and remain to be a key problem. In this paper, we use the tensor minimax probability (TMPM) to prove that the tensor representation is stable. The proof is based on the random subspace method through a large number of experiments.\",\"PeriodicalId\":213856,\"journal\":{\"name\":\"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2005.136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2005.136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stable third-order tensor representation for colour image classification
General tensors can represent colour images more naturally than conventional features; however, the general tensors' stability properties are not reported and remain to be a key problem. In this paper, we use the tensor minimax probability (TMPM) to prove that the tensor representation is stable. The proof is based on the random subspace method through a large number of experiments.
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