{"title":"基于模糊线性判别分析的多视图图学习","authors":"Hong Jia, Jian Huang","doi":"10.1145/3579654.3579708","DOIUrl":null,"url":null,"abstract":"The multi-view clustering method based on graph learning has been extensively studied because of its good clustering effect. However, most of the graph learning methods are based on the original data features, which often contain noise and outliers, and using them directly may lead to learning suboptimal graph. To address the above problems, we propose a multi-view graph learning method based on linear discriminant analysis. On the one hand, the manifold structure obtained using the global graph guides the learning of the discriminative projections for each view, and on the other hand, the common graph is learned using the projection data of each view. Then, an efficient solution is proposed by dividing the problem into two sub-problems. Finally, experiments are conducted on some public multi-view datasets to verify the effectiveness of the method.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis\",\"authors\":\"Hong Jia, Jian Huang\",\"doi\":\"10.1145/3579654.3579708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The multi-view clustering method based on graph learning has been extensively studied because of its good clustering effect. However, most of the graph learning methods are based on the original data features, which often contain noise and outliers, and using them directly may lead to learning suboptimal graph. To address the above problems, we propose a multi-view graph learning method based on linear discriminant analysis. On the one hand, the manifold structure obtained using the global graph guides the learning of the discriminative projections for each view, and on the other hand, the common graph is learned using the projection data of each view. Then, an efficient solution is proposed by dividing the problem into two sub-problems. Finally, experiments are conducted on some public multi-view datasets to verify the effectiveness of the method.\",\"PeriodicalId\":146783,\"journal\":{\"name\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3579654.3579708\",\"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 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579654.3579708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis
The multi-view clustering method based on graph learning has been extensively studied because of its good clustering effect. However, most of the graph learning methods are based on the original data features, which often contain noise and outliers, and using them directly may lead to learning suboptimal graph. To address the above problems, we propose a multi-view graph learning method based on linear discriminant analysis. On the one hand, the manifold structure obtained using the global graph guides the learning of the discriminative projections for each view, and on the other hand, the common graph is learned using the projection data of each view. Then, an efficient solution is proposed by dividing the problem into two sub-problems. Finally, experiments are conducted on some public multi-view datasets to verify the effectiveness of the method.