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ICDM
2010
IEEE
197views Data Mining» more  ICDM 2010»
9 years 7 months ago
D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification
: D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi HP Labo...
Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yu...
PR
2010
156views more  PR 2010»
9 years 8 months ago
Semi-supervised clustering with metric learning: An adaptive kernel method
Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel met...
Xuesong Yin, Songcan Chen, Enliang Hu, Daoqiang Zh...
PR
2006
164views more  PR 2006»
9 years 9 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
ICASSP
2010
IEEE
9 years 9 months ago
Learning from high-dimensional noisy data via projections onto multi-dimensional ellipsoids
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
Liuling Gong, Dan Schonfeld
SDM
2004
SIAM
225views Data Mining» more  SDM 2004»
9 years 11 months ago
Active Semi-Supervision for Pairwise Constrained Clustering
Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constra...
Sugato Basu, Arindam Banerjee, Raymond J. Mooney
EWIMT
2004
9 years 11 months ago
Fuzzy Clustering with Pairwise Constraints for Knowledge-Driven Image Categorization
The identification of categories in image databases usually relies on clustering algorithms that only exploit the feature-based similarities between images. The addition of semant...
Nizar Grira, Michel Crucianu, Nozha Boujemaa
MLMTA
2007
9 years 11 months ago
A Novel Hybrid Neural Network for Data Clustering
- Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer either poor performance of unsupervised lea...
Donghai Guan, Andrey Gavrilov, Weiwei Yuan, Young-...
SDM
2008
SIAM
168views Data Mining» more  SDM 2008»
9 years 11 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
CIVR
2008
Springer
125views Image Analysis» more  CIVR 2008»
9 years 11 months ago
Leveraging user query log: toward improving image data clustering
Image clustering is useful in many retrieval and classification applications. The main goal of image clustering is to partition a given dataset into salient clusters such that the...
Hao Cheng, Kien A. Hua, Khanh Vu
GECCO
2006
Springer
144views Optimization» more  GECCO 2006»
10 years 1 months ago
On semi-supervised clustering via multiobjective optimization
Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clu...
Julia Handl, Joshua D. Knowles
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