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» Nonlinear principal component analysis of noisy data
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ICML
2007
IEEE
15 years 10 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...
CORR
2010
Springer
208views Education» more  CORR 2010»
14 years 6 months ago
Real-time Robust Principal Components' Pursuit
In the recent work of Candes et al, the problem of recovering low rank matrix corrupted by i.i.d. sparse outliers is studied and a very elegant solution, principal component pursui...
Chenlu Qiu, Namrata Vaswani
CSDA
2004
105views more  CSDA 2004»
14 years 9 months ago
Computational aspects of algorithms for variable selection in the context of principal components
Variable selection consists in identifying a k-subset of a set of original variables that is optimal for a given criterion of adequate approximation to the whole data set. Several...
Jorge Cadima, J. Orestes Cerdeira, Manuel Minhoto
BMCBI
2011
14 years 1 months ago
Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and STRUCTU
Background: The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used ...
Tulaya Limpiti, Apichart Intarapanich, Anunchai As...
PAMI
2012
13 years 2 days ago
A Least-Squares Framework for Component Analysis
— Over the last century, Component Analysis (CA) methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Lap...
Fernando De la Torre