Results 21 to 30 of about 7,354 (203)
kPCA-Based Parametric Solutions Within the PGD Framework [PDF]
Archives of Computational Methods in Engineering, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
González, D. +4 more
openaire +5 more sources
IEEE Access, 2020 Kernel Principal Component Analysis (KPCA) using Radial Basis Function (RBF) kernels can capture data nonlinearity by projecting the original variable space to a high-dimensional kernel feature space and obtaining the kernel principal components.
Ruomu Tan +2 more
doaj +1 more source
Training Echo State Networks with Regularization through Dimensionality Reduction [PDF]
, 2016 In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series. The method consists in projecting the output of the internal layer of the network on a space with lower dimensionality, before training the ...
Bianchi, Filippo Maria +2 more
core +2 more sources
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach [PDF]
, 2015 Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space embedded in a ...
Alavi, Azadeh +3 more
core +2 more sources
Optimized kernel minimum noise fraction transformation for hyperspectral image classification [PDF]
, 2017 This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear ...
Gao, Lianru +4 more
core +2 more sources
Kernel principal component analysis (KPCA) for the de-noising of communication signals [PDF]
, 2002 This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a ...
Koutsogiannis, G., Soraghan, J.J.
core +2 more sources
Speaker Recognition Based on KPCA and KFCM [PDF]
Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering, 2015 Speaker recognition system can identify a certain person using speech analysis. Recent advances in speech processing techniques improve the recognition rate. In this paper, an efficient speaker recognition system is proposed. Firstly, a KPCA-based feature selection approach is adopted to get the efficiently reduced dimension of feature vectors and ...
Yuanyuan Zhang, Jian Wang
openaire +1 more source
Two-Phase Incremental Kernel PCA for Learning Massive or Online Datasets
Complexity, 2019 As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling ...
Feng Zhao +5 more
doaj +1 more source
Feature level quantitative ultrasound and CT information fusion to predict the outcome of head & neck cancer radiotherapy treatment: Enhanced principal component analysis. [PDF]
Med PhysAbstract Background Radiation therapy is a common treatment for head and neck (H&N) cancers. Radiomic features, which are determined from biomedical imaging, can be effective biomarkers used to assess tumor heterogeneity and have been used to predict response to treatment.
Moslemi A +4 more
europepmc +2 more sources
IEEE Access, 2017 Kernel principal component analysis (KPCA) has been a state-of-the-art nonlinear process monitoring method. However, KPCA assumes the single operation mode while the real industrial processes often run under multiple operation conditions.
Xiaogang Deng, Na Zhong, Lei Wang
doaj +1 more source