Kernel maximum autocorrelation factor and minimum noise fraction transformations
AA Nielsen - IEEE Transactions on Image Processing, 2010 - ieeexplore.ieee.org
This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and
minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual …
minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual …
Circuit depth reduction for gate-model quantum computers
L Gyongyosi, S Imre - Scientific reports, 2020 - nature.com
Quantum computers utilize the fundamentals of quantum mechanics to solve computational
problems more efficiently than traditional computers. Gate-model quantum computers are …
problems more efficiently than traditional computers. Gate-model quantum computers are …
Preimage problem in kernel-based machine learning
P Honeine, C Richard - IEEE Signal Processing Magazine, 2011 - ieeexplore.ieee.org
While the nonlinear mapping from the input space to the feature space is central in kernel
methods, the reverse mapping from the feature space back to the input space is also of …
methods, the reverse mapping from the feature space back to the input space is also of …
Regularized kernel PCA for the efficient parameterization of complex geological models
HX Vo, LJ Durlofsky - Journal of Computational Physics, 2016 - Elsevier
The use of geological parameterization procedures enables high-fidelity geomodels to be
represented in terms of relatively few variables. Such parameterizations are particularly …
represented in terms of relatively few variables. Such parameterizations are particularly …
Nonlinear denoising and analysis of neuroimages with kernel principal component analysis and pre-image estimation
PM Rasmussen, TJ Abrahamsen, KH Madsen… - NeuroImage, 2012 - Elsevier
We investigate the use of kernel principal component analysis (PCA) and the inverse
problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre …
problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre …
Non-linear dimensionality regularizer for solving inverse problems
Consider an ill-posed inverse problem of estimating causal factors from observations, one of
which is known to lie near some (un-known) low-dimensional, non-linear manifold …
which is known to lie near some (un-known) low-dimensional, non-linear manifold …
The pre-image problem and kernel PCA for speech enhancement
C Leitner, F Pernkopf - Advances in Nonlinear Speech Processing: 5th …, 2011 - Springer
In this paper, we use kernel principal component analysis (kPCA) for speech enhancement.
To synthesize the de-noised audio signal we rely on an iterative pre-image method. In order …
To synthesize the de-noised audio signal we rely on an iterative pre-image method. In order …
Fast explanation of RBF-Kernel SVM models using activation patterns
M Zhang, M Treder, D Marshall… - 2024 International Joint …, 2024 - ieeexplore.ieee.org
Machine learning models have significantly enriched the toolbox in the field of neuroimaging
analysis. Among them, Support Vector Machines (SVM) have been one of the most popular …
analysis. Among them, Support Vector Machines (SVM) have been one of the most popular …
Speech enhancement using pre-image iterations
C Leitner, F Pernkopf - 2012 IEEE International Conference on …, 2012 - ieeexplore.ieee.org
In this paper, we present a new method to de-noise speech in the complex spectral domain.
The method is derived from kernel principal component analysis (kPCA). Instead of applying …
The method is derived from kernel principal component analysis (kPCA). Instead of applying …
Automatic age progression and estimation from faces
AM Bukar - 2019 - bradscholars.brad.ac.uk
Recently, automatic age progression has gained popularity due to its numerous
applications. Among these is the frequent search for missing people, in the UK alone up to …
applications. Among these is the frequent search for missing people, in the UK alone up to …