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» Metric and Kernel Learning Using a Linear Transformation
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JMLR
2012
8 years 2 months ago
Metric and Kernel Learning Using a Linear Transformation
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
Prateek Jain, Brian Kulis, Jason V. Davis, Inderji...
IVC
2007
176views more  IVC 2007»
9 years 11 months ago
Kernel-based distance metric learning for content-based image retrieval
ct 8 For a speciļ¬c set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system 9 depends critically on the similarity or diss...
Hong Chang, Dit-Yan Yeung
ICMLC
2005
Springer
10 years 5 months ago
Kernel-Based Metric Adaptation with Pairwise Constraints
Abstract. Many supervised and unsupervised learning algorithms depend on the choice of an appropriate distance metric. While metric learning for supervised learning tasks has a lon...
Hong Chang, Dit-Yan Yeung
PR
2006
164views more  PR 2006»
9 years 11 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
KDD
2007
ACM
276views Data Mining» more  KDD 2007»
10 years 12 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
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