116views more  PR 2006»
12 years 9 months ago
Correspondence matching using kernel principal components analysis and label consistency constraints
This paper investigates spectral approaches to the problem of point pattern matching. We make two contributions. First, we consider rigid point-set alignment. Here we show how ker...
Hongfang Wang, Edwin R. Hancock
144views more  PAMI 2008»
12 years 9 months ago
Twin Kernel Embedding
Visualization of non-vectorial objects is not easy in practice due to their lack of convenient vectorial representation. Representative approaches are Kernel PCA and Kernel Laplac...
Yi Guo, Junbin Gao, Paul W. Kwan
131views more  JMLR 2008»
12 years 9 months ago
On Relevant Dimensions in Kernel Feature Spaces
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
139views more  CSDA 2010»
12 years 9 months ago
Detecting influential observations in Kernel PCA
Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivit...
Michiel Debruyne, Mia Hubert, Johan Van Horebeek
12 years 11 months ago
Eigenvoice Speaker Adaptation via Composite Kernel PCA
Eigenvoice speaker adaptation has been shown to be effective when only a small amount of adaptation data is available. At the heart of the method is principal component analysis (...
James T. Kwok, Brian Mak, Simon Ho
166views more  WSCG 2004»
12 years 11 months ago
De-noising and Recovering Images Based on Kernel PCA Theory
Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...
Pengcheng Xi, Tao Xu
133views Computer Vision» more  MVA 2007»
12 years 11 months ago
Selection of Object Recognition Methods According to the Task and Object Category
Service robots need object recognition strategy that can work on various objects in complex backgrounds. Since no single method can work in every situation, we need to combine sev...
Al Mansur, Yoshinori Kuno
12 years 11 months ago
Kernel PCA based clustering for inducing features in text categorization
We study dimensionality reduction or feature selection in text document categorization problem. We focus on the first step in building text categorization systems, that is the cho...
Zsolt Minier, Lehel Csató
13 years 3 months ago
A Statistical Mechanics Analysis of Gram Matrix Eigenvalue Spectra
Abstract. The Gram matrix plays a central role in many kernel methods. Knowledge about the distribution of eigenvalues of the Gram matrix is useful for developing appropriate model...
David C. Hoyle, Magnus Rattray
182views Multimedia» more  ICMCS 2005»
13 years 3 months ago
An integrated approach for generic object detection using kernel PCA and boosting
In this paper we present a novel framework for generic object class detection by integrating Kernel PCA with AdaBoost. The classifier obtained in this way is invariant to changes...
Saad Ali, Mubarak Shah