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TKDE
2011
479views more  TKDE 2011»
8 years 6 months ago
Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
—Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feat...
Wei Zhang, Zhouchen Lin, Xiaoou Tang
CVPR
2008
IEEE
9 years 28 days ago
Classification via semi-Riemannian spaces
In this paper, we develop a geometric framework for linear or nonlinear discriminant subspace learning and classification. In our framework, the structures of classes are conceptu...
Deli Zhao, Zhouchen Lin, Xiaoou Tang
ICMLC
2005
Springer
9 years 4 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
ICDM
2008
IEEE
109views Data Mining» more  ICDM 2008»
9 years 5 months ago
Learning by Propagability
In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and o...
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, Loon...
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
9 years 5 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
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