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» PCA in Autocorrelation Space
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ICIP
2008
IEEE
14 years 9 months ago
Principal components for non-local means image denoising
This paper presents an image denoising algorithm that uses principal component analysis (PCA) in conjunction with the non-local means image denoising. Image neighborhood vectors u...
Tolga Tasdizen
ICML
2003
IEEE
14 years 8 months ago
A Kernel Between Sets of Vectors
In various application domains, including image recognition, it is natural to represent each example as a set of vectors. With a base kernel we can implicitly map these vectors to...
Risi Imre Kondor, Tony Jebara
ICML
2003
IEEE
14 years 8 months ago
Kernel PLS-SVC for Linear and Nonlinear Classification
A new method for classification is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by a ...
Roman Rosipal, Leonard J. Trejo, Bryan Matthews
ICPR
2010
IEEE
14 years 2 months ago
Gait Learning-Based Regenerative Model: A Level Set Approach
We propose a learning method for gait synthesis from a sequence of shapes(frames) with the ability to extrapolate to novel data. It involves the application of PCA, first to redu...
Muayed Sattar Al-Huseiny, Sasan Mahmoodi, Mark Nix...
AAAI
2004
13 years 9 months ago
Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo
Based on the probabilistic reformulation of principal component analysis (PCA), we consider the problem of determining the number of principal components as a model selection prob...
Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan...