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» Sufficient covariates and linear propensity analysis
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JMLR
2010
93views more  JMLR 2010»
13 years 2 months ago
Sufficient covariates and linear propensity analysis
Working within the decision-theoretic framework for causal inference, we study the properties of "sufficient covariates", which support causal inference from observation...
Hui Guo, A. Philip Dawid
CIARP
2006
Springer
13 years 11 months ago
A Theoretical Comparison of Two Linear Dimensionality Reduction Techniques
Abstract. A theoretical analysis for comparing two linear dimensionality reduction (LDR) techniques, namely Fisher's discriminant (FD) and Loog-Duin (LD) dimensionality reduci...
Luis Rueda, Myriam Herrera
ICCV
2007
IEEE
14 years 9 months ago
Semi-supervised Discriminant Analysis
Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection vectors are commonly obtained by maximizing ...
Deng Cai, Xiaofei He, Jiawei Han
ICML
2007
IEEE
14 years 8 months ago
Full regularization path for sparse principal component analysis
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the numb...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
ICML
2007
IEEE
14 years 8 months ago
Regression on manifolds using kernel dimension reduction
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...
Jens Nilsson, Fei Sha, Michael I. Jordan