{"title":"约束条件下的盲识别问题","authors":"A. Cichocki, P. Georgiev","doi":"10.1109/NNSP.2002.1030065","DOIUrl":null,"url":null,"abstract":"In many applications of independent component analysis (ICA) and blind source separation (BSS) the mixing or separating matrices have some special structure or some constraints are imposed for the matrices like symmetry, orthogonality, nonnegativity, sparseness and unit (or specified invariant norm) of the matrix. We present several algorithms and overview some known transformations which allows us to preserve such constraints. Especially, we propose algorithms for a blind identification problem with non-negativity constraints.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Blind identification problems with constraints\",\"authors\":\"A. Cichocki, P. Georgiev\",\"doi\":\"10.1109/NNSP.2002.1030065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many applications of independent component analysis (ICA) and blind source separation (BSS) the mixing or separating matrices have some special structure or some constraints are imposed for the matrices like symmetry, orthogonality, nonnegativity, sparseness and unit (or specified invariant norm) of the matrix. We present several algorithms and overview some known transformations which allows us to preserve such constraints. Especially, we propose algorithms for a blind identification problem with non-negativity constraints.\",\"PeriodicalId\":117945,\"journal\":{\"name\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.2002.1030065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.2002.1030065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In many applications of independent component analysis (ICA) and blind source separation (BSS) the mixing or separating matrices have some special structure or some constraints are imposed for the matrices like symmetry, orthogonality, nonnegativity, sparseness and unit (or specified invariant norm) of the matrix. We present several algorithms and overview some known transformations which allows us to preserve such constraints. Especially, we propose algorithms for a blind identification problem with non-negativity constraints.
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