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» Mining Subspace Correlations
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KDD
2004
ACM
187views Data Mining» more  KDD 2004»
14 years 8 months ago
IMMC: incremental maximum margin criterion
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or la...
Jun Yan, Benyu Zhang, Shuicheng Yan, Qiang Yang, H...
WCNC
2008
IEEE
14 years 2 months ago
Subspace Blind MIMO-OFDM Channel Estimation with Short Averaging Periods: Performance Analysis
—Among all blind channel estimation problems, subspace-based algorithms are attractive due to its fastconverging nature. It primarily exploits the orthogonality structure of the ...
Chao-Cheng Tu, Benoît Champagne
TSP
2008
166views more  TSP 2008»
13 years 7 months ago
A Unifying Discussion of Correlation Analysis for Complex Random Vectors
The assessment of multivariate association between two complex random vectors is considered. A number of correlation coefficients based on three popular correlation analysis techni...
Peter J. Schreier
ICDM
2003
IEEE
184views Data Mining» more  ICDM 2003»
14 years 28 days ago
Analyzing High-Dimensional Data by Subspace Validity
We are proposing a novel method that makes it possible to analyze high dimensional data with arbitrary shaped projected clusters and high noise levels. At the core of our method l...
Amihood Amir, Reuven Kashi, Nathan S. Netanyahu, D...
ICDM
2005
IEEE
151views Data Mining» more  ICDM 2005»
14 years 1 months ago
A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria
In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-const...
Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M...