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dc.contributor.authorQian, Jingen_US
dc.date.accessioned2015-08-18T17:14:48Z
dc.date.available2015-08-18T17:14:48Z
dc.date.issued2014
dc.date.submitted2014
dc.identifier.other(ALMA)contemp
dc.identifier.urihttps://hdl.handle.net/2144/12951
dc.descriptionThesis (Ph.D.)--Boston Universityen_US
dc.description.abstractIn machine learning, the problem of unsupervised learning is that of trying to explain key features and find hidden structures in unlabeled data. In this thesis we focus on three unsupervised learning scenarios: graph based clustering with imbalanced data, point-wise anomaly detection and anomalous cluster detection on graphs. In the first part we study spectral clustering, a popular graph based clustering technique. We investigate the reason why spectral clustering performs badly on imbalanced and proximal data. We then propose the partition constrained minimum cut (PCut) framework based on a novel parametric graph construction method, that is shown to adapt to different degrees of imbalanced data. We analyze the limit cut behavior of our approach, and demonstrate the significant performance improvement through clustering and semi-supervised learning experiments on imbalanced data. [TRUNCATED]en_US
dc.language.isoen_US
dc.publisherBoston Universityen_US
dc.titleUnsupervised learning in high-dimensional spaceen_US
dc.typeThesis/Dissertationen_US
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineSystems Engineeringen_US
etd.degree.grantorBoston Universityen_US


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