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ICIP
2005
IEEE
14 years 9 months ago
Largest-eigenvalue-theory for incremental principal component analysis
In this paper, we present a novel algorithm for incremental principal component analysis. Based on the LargestEigenvalue-Theory, i.e. the eigenvector associated with the largest ei...
Shuicheng Yan, Xiaoou Tang
PR
2006
115views more  PR 2006»
13 years 7 months ago
Diagonal principal component analysis for face recognition
In this paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks t...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
CVPR
2008
IEEE
14 years 9 months ago
Parameterized Kernel Principal Component Analysis: Theory and applications to supervised and unsupervised image alignment
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
Fernando De la Torre, Minh Hoai Nguyen
DATASCIENCE
2002
84views more  DATASCIENCE 2002»
13 years 7 months ago
The application of Principal Component Analysis to materials science data
The relationship between apparently disparate sets of data is a critical component of interpreting materials' behavior, especially in terms of assessing the impact of the mic...
Changwon Suh, Arun Rajagopalan, Xiang Li, Krishna ...
COMPLIFE
2006
Springer
13 years 11 months ago
Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models
We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov model...
Illia Horenko, Johannes Schmidt-Ehrenberg, Christo...