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IJCV
2008
155views more  IJCV 2008»
14 years 9 months ago
Fast Transformation-Invariant Component Analysis
For software and more illustrations: http://www.psi.utoronto.ca/anitha/fastTCA.htm Dimensionality reduction techniques such as principal component analysis and factor analysis are...
Anitha Kannan, Nebojsa Jojic, Brendan J. Frey
BMCBI
2006
202views more  BMCBI 2006»
14 years 9 months ago
Spectral embedding finds meaningful (relevant) structure in image and microarray data
Background: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing ...
Brandon W. Higgs, Jennifer W. Weller, Jeffrey L. S...
ISNN
2007
Springer
15 years 3 months ago
Two-Dimensional Bayesian Subspace Analysis for Face Recognition
Bayesian subspace analysis (BSA) has been successfully applied in data mining and pattern recognition. However, due to the use of probabilistic measure of similarity, it often need...
Daoqiang Zhang
WEBI
2010
Springer
14 years 7 months ago
DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...
Su Yan, Sofien Bouaziz, Dongwon Lee
NIPS
2004
14 years 11 months ago
Two-Dimensional Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional d...
Jieping Ye, Ravi Janardan, Qi Li