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ICDM
2009
IEEE
120views Data Mining» more  ICDM 2009»
16 years 1 months ago
Least Square Incremental Linear Discriminant Analysis
Abstract—Linear discriminant analysis (LDA) is a wellknown dimension reduction approach, which projects highdimensional data into a low-dimensional space with the best separation...
Li-Ping Liu, Yuan Jiang, Zhi-Hua Zhou
ICDM
2009
IEEE
132views Data Mining» more  ICDM 2009»
16 years 1 months ago
Bayesian Overlapping Subspace Clustering
Given a data matrix, the problem of finding dense/uniform sub-blocks in the matrix is becoming important in several applications. The problem is inherently combinatorial since th...
Qiang Fu, Arindam Banerjee
PKDD
2009
Springer
120views Data Mining» more  PKDD 2009»
16 years 1 months ago
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn fea...
Shuang-Hong Yang, Hongyuan Zha, Shaohua Kevin Zhou...
PKDD
2009
Springer
149views Data Mining» more  PKDD 2009»
16 years 1 months ago
Learning to Disambiguate Search Queries from Short Sessions
Web searches tend to be short and ambiguous. It is therefore not surprising that Web query disambiguation is an actively researched topic. To provide a personalized experience for ...
Lilyana Mihalkova, Raymond J. Mooney
ICDM
2008
IEEE
160views Data Mining» more  ICDM 2008»
16 years 1 months ago
Direct Zero-Norm Optimization for Feature Selection
Zero-norm, defined as the number of non-zero elements in a vector, is an ideal quantity for feature selection. However, minimization of zero-norm is generally regarded as a combi...
Kaizhu Huang, Irwin King, Michael R. Lyu