Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann
{"title":"Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings","authors":"Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann","doi":"10.1145/3097983.3098156","DOIUrl":null,"url":null,"abstract":"Spectral clustering is one of the most prominent clustering approaches. However, it is highly sensitive to noisy input data. In this work, we propose a robust spectral clustering technique able to handle such scenarios. To achieve this goal, we propose a sparse and latent decomposition of the similarity graph used in spectral clustering. In our model, we jointly learn the spectral embedding as well as the corrupted data - thus, enhancing the clustering performance overall. We propose algorithmic solutions to all three established variants of spectral clustering, each showing linear complexity in the number of edges. Our experimental analysis confirms the significant potential of our approach for robust spectral clustering. Supplementary material is available at www.kdd.in.tum.de/RSC.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"60","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3097983.3098156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 60
Abstract
Spectral clustering is one of the most prominent clustering approaches. However, it is highly sensitive to noisy input data. In this work, we propose a robust spectral clustering technique able to handle such scenarios. To achieve this goal, we propose a sparse and latent decomposition of the similarity graph used in spectral clustering. In our model, we jointly learn the spectral embedding as well as the corrupted data - thus, enhancing the clustering performance overall. We propose algorithmic solutions to all three established variants of spectral clustering, each showing linear complexity in the number of edges. Our experimental analysis confirms the significant potential of our approach for robust spectral clustering. Supplementary material is available at www.kdd.in.tum.de/RSC.