Results 91 to 100 of about 29,752 (189)

Una técnica robusta para Kernel PCA

, 2005
Kernel PCA generaliza el Análisis de Componentes Principales (PCA) a dominios no-lineales.
Mora Forsbach, Luis Ernesto
core   +1 more source

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.
Debruyne, Michiel, Hubert, Mia, Van Horebeek, Johan   +2 more
core  

Eigenvoice speaker adaptation via composite kernel PCA

, 2004
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 (PCA) employed to find the most important eigenvoices.
Kwok, James T., Ho, Simon, Mak, Brian
core  

kernlab - An S4 Package for Kernel Methods in R

kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels),
Kurt Hornik, Alexandros Smola, Achim Zeileis, Alexandros Karatzoglou   +3 more
core  

Application in soft sensing modeling of chemical processbased on K-OPLS method

Journal of Measurement Science and Instrumentation, 2020
Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity, a soft sensing modeling method based on kernel-based orthogonal projections to latent structures（K-OPLS） is proposed.
LI Jun, LI Kai
doaj  

Principal component analysis for angular data using kernel PCA.

, 2014
A. Centered feature-feature similarity matrix according to eq. (6) for the sperm tangent angle data. B. First shape mode for the kernel method (green) compared to the first shape mode as obtained by linear PCA (blue dashed). C.
Benjamin M. Friedrich (159460), Ingmar H. Riedel-Kruse (666348), Steffen Werner (666347), Jochen C. Rink (123746)   +3 more
core   +1 more source

Non-Invasive Screening for Early Cognitive Impairment in Elderly Hyperuricaemic Men Using Transcranial Colour-Coded Duplex Sonography

Diagnostics
Objectives: Hyperuricaemia has been linked to cognitive decline, yet cerebral structural and haemodynamic changes in this population remain poorly defined.
Zhirong Xu, Jiayi Ye, Han Wang, Jiemin Chen, Kailing Tan, Shilin Li, Shanshan Su   +6 more
doaj   +1 more source

Nonlinear dynamic process monitoring based on dynamic kernel PCA

, 2019
Nonlinear dynamic process monitoring based on dynamic kernel principal component analysis (DKPCA) is proposed. The kernel functions used in kernel PCA (KPCA) are profitable for capturing nonlinear property of processes and the time-lagged data extension ...
Choi, SW, Lee, IB
core   +1 more source

De-noising and recovering images based on Kernel PCA theory

, 2004
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 are called principal components.
Xu, Tao, Xi, Pengcheng
core  

Shallow Representation Learning via Kernel PCA Improves QSAR Modelability. [PDF]

J Chem Inf Model, 2017
Rensi SE, Altman RB.
europepmc   +1 more source