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JMLR
2008
131views more  JMLR 2008»
14 years 9 months ago
On Relevant Dimensions in Kernel Feature Spaces
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
SIGMOD
2003
ACM
237views Database» more  SIGMOD 2003»
15 years 9 months ago
Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval
The learning-enhanced relevance feedback has been one of the most active research areas in content-based image retrieval in recent years. However, few methods using the relevance ...
Deok-Hwan Kim, Chin-Wan Chung
IVC
2007
176views more  IVC 2007»
14 years 9 months ago
Kernel-based distance metric learning for content-based image retrieval
ct 8 For a specific 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
IJCAI
2007
14 years 11 months ago
A Subspace Kernel for Nonlinear Feature Extraction
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used pre-processing step in pattern classification and data mining tasks. Given a positive...
Mingrui Wu, Jason D. R. Farquhar
ICIP
2003
IEEE
15 years 11 months ago
Kernel indexing for relevance feedback image retrieval
Relevance feedback is an attractive approach to developing flexible metrics for content-based retrieval in image and video databases. Large image databases require an index struct...
Jing Peng, Douglas R. Heisterkamp