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JMLR
2012
13 years 2 months ago
Sparse Higher-Order Principal Components Analysis
Traditional tensor decompositions such as the CANDECOMP / PARAFAC (CP) and Tucker decompositions yield higher-order principal components that have been used to understand tensor d...
Genevera Allen
128
Voted
ICPR
2006
IEEE
15 years 5 months ago
Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis
In this paper, we address the pre-image problem in kernel principal component analysis (KPCA). The preimage problem finds a pattern as the pre-image of a feature vector defined in...
Weishi Zheng, Jian-Huang Lai
ECML
2007
Springer
15 years 5 months ago
Principal Component Analysis for Large Scale Problems with Lots of Missing Values
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
Tapani Raiko, Alexander Ilin, Juha Karhunen
HAPTICS
2007
IEEE
15 years 6 months ago
Finger Force Direction Recognition by Principal Component Analysis of Fingernail Coloration Pattern
A method based on Principal Component Analysis of the fingernail coloration pattern is presented to infer fingertip force direction during planar contact. Images from 7 subjects...
Yu Sun, John M. Hollerbach, Stephen A. Mascaro
BMCBI
2011
14 years 3 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...