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ECML
2006
Springer
13 years 11 months ago
An Adaptive Kernel Method for Semi-supervised Clustering
Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introd...
Bojun Yan, Carlotta Domeniconi
UAI
2008
13 years 8 months ago
Flexible Priors for Exemplar-based Clustering
Exemplar-based clustering methods have been shown to produce state-of-the-art results on a number of synthetic and real-world clustering problems. They are appealing because they ...
Daniel Tarlow, Richard S. Zemel, Brendan J. Frey
AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
14 years 1 months ago
A Discriminant Analysis for Undersampled Data
One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pai...
Matthew Robards, Junbin Gao, Philip Charlton
ICIP
2004
IEEE
14 years 9 months ago
Detection of unique people in news programs using multimodal shot clustering
In this paper we describe an approach that uses a combination of visual and audio features to cluster shots belonging to the same person together in video programs. We use color h...
Alberto Albiol, Cüneyt M. Taskiran, Edward J....
SDM
2008
SIAM
168views Data Mining» more  SDM 2008»
13 years 8 months ago
Semi-Supervised Clustering via Matrix Factorization
The recent years have witnessed a surge of interests of semi-supervised clustering methods, which aim to cluster the data set under the guidance of some supervisory information. U...
Fei Wang, Tao Li, Changshui Zhang