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» Adaptive Distance Metric Learning for Clustering
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CVPR
2007
IEEE
14 years 6 months ago
Adaptive Distance Metric Learning for Clustering
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...
Jieping Ye, Zheng Zhao, Huan Liu
KDD
2007
ACM
276views Data Mining» more  KDD 2007»
14 years 5 months ago
Nonlinear adaptive distance metric learning for clustering
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
PR
2006
164views more  PR 2006»
13 years 4 months ago
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the...
Hong Chang, Dit-Yan Yeung
PR
2006
141views more  PR 2006»
13 years 4 months ago
Relaxational metric adaptation and its application to semi-supervised clustering and content-based image retrieval
The performance of many supervised and unsupervised learning algorithms is very sensitive to the choice of an appropriate distance metric. Previous work in metric learning and ada...
Hong Chang, Dit-Yan Yeung, William K. Cheung
ICML
2005
IEEE
14 years 5 months ago
A new Mallows distance based metric for comparing clusterings
Despite of the large number of algorithms developed for clustering, the study on comparing clustering results is limited. In this paper, we propose a measure for comparing cluster...
Ding Zhou, Jia Li, Hongyuan Zha