Results 1 - 10 of 8,620

Kernel independent component analysis

by Francis R. Bach - Journal of Machine Learning Research , 2002
"... We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical propert ..."
Abstract - Cited by 464 (24 self) - Add to MetaCart
We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical

Separation of Reflection Components by Kernel Independent Component Analysis

by Masaki Yamazaki, Yen-wei Chen, Gang Xu , 2006
"... When we view a scene through transparent glass, the image is a linear superposition of two images, a real image observed through a glass and a virtual image reflected on it. We can separate the reflections by a polarization and Independent Component Analysis (ICA). Since the image observed through d ..."
Abstract - Add to MetaCart
digital camera is non-linearly transformed by gamma correction etc, it may cause error in image processing for image analysis and measurement. The kernel-based methods are effective for such non-linearity. In this paper, we remove the reflections by using Kernel Independent Component Analysis (KICA

An information-theoretic perspective to kernel independent component analysis

by Jian-wu Xu, Deniz Erdogmus, Robert Jenssen, Jose C. Principe - in ICASSP 2005 , 2004
"... In this paper, we investigate the intriguing relationship between information-theoretic learning (ITL), based on weighted Parzen window density estimator, and kernel-based learning algorithms. We prove the equivalence between kernel independent component analysis (KERNEL ICA) and Cauchy-Schwartz (C- ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
In this paper, we investigate the intriguing relationship between information-theoretic learning (ITL), based on weighted Parzen window density estimator, and kernel-based learning algorithms. We prove the equivalence between kernel independent component analysis (KERNEL ICA) and Cauchy-Schwartz (C

Survey on Independent Component Analysis

by Aapo Hyvärinen - NEURAL COMPUTING SURVEYS , 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
Abstract - Cited by 2309 (104 self) - Add to MetaCart
of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes

Nonlinear component analysis as a kernel eigenvalue problem

by Bernhard Schölkopf, Alexander Smola, Klaus-Robert Müller - , 1996
"... We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all ..."
Abstract - Cited by 1573 (83 self) - Add to MetaCart
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all

Independent component analysis: algorithms and applications

by A. Hyvärinen, E. Oja - NEURAL NETWORKS , 2000
"... ..."
Abstract - Cited by 851 (10 self) - Add to MetaCart
Abstract not found

Fast and robust fixed-point algorithms for independent component analysis

by Aapo Hyvärinen - IEEE TRANS. NEURAL NETW , 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
Abstract - Cited by 884 (34 self) - Add to MetaCart
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s

The "Independent Components" of Natural Scenes are Edge Filters

by Anthony J. Bell, Terrence J. Sejnowski , 1997
"... It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm that attem ..."
Abstract - Cited by 617 (29 self) - Add to MetaCart
. Some of these filters are Gabor-like and resemble those produced by the sparseness-maximization network. In addition, the outputs of these filters are as independent as possible, since this infomax network performs Independent Components Analysis or ICA, for sparse (super-gaussian) component

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

by Arnaud Delorme, Scott Makeig - J. Neurosci. Methods
"... Abstract: We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event i ..."
Abstract - Cited by 886 (45 self) - Add to MetaCart
information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel

Multipoint quantitative-trait linkage analysis in general pedigrees

by Laura Almasy, John Blangero - Am. J. Hum. Genet , 1998
"... Multipoint linkage analysis of quantitative-trait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variance-component linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint i ..."
Abstract - Cited by 567 (60 self) - Add to MetaCart
or by multiple independent lines of descent. Once calculated, the mul-tipoint relative-pair IBDs can be utilized in variance-component linkage analysis, which considers the likeli-hood of the entire pedigree jointly. Examples are given that use simulated data, demonstrating both the accu-racy of QTL localization
Results 1 - 10 of 8,620