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NECO
1998
151views more  NECO 1998»
13 years 10 months ago
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
TNN
2011
200views more  TNN 2011»
13 years 5 months ago
Domain Adaptation via Transfer Component Analysis
Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. Intuitively, discovering a good feature rep...
Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qi...
FMCAD
2000
Springer
14 years 2 months ago
An Algorithm for Strongly Connected Component Analysis in n log n Symbolic Steps
We present a symbolic algorithm for strongly connected component decomposition. The algorithm performs (n log n) image and preimage computations in the worst case, where n is the n...
Roderick Bloem, Harold N. Gabow, Fabio Somenzi
CVPR
2008
IEEE
15 years 23 days 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
ECCV
2002
Springer
15 years 18 days ago
Robust Parameterized Component Analysis
Principal ComponentAnalysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion. In particular, PCA has been widely used to model the var...
Fernando De la Torre, Michael J. Black