Results 41 to 50 of about 4,584 (156)

Gene- or region-based association study via kernel principal component analysis

BMC Genetics, 2011
Background In genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits).
Zhao Jinghua, Yuan Zhongshang, He Yungang, Gao Qingsong, Zhang Bingbing, Xue Fuzhong   +5 more
doaj   +1 more source

Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data [PDF]

, 2017
Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or ...
Bianchi, Filippo Maria, Jenssen, Robert, Mikalsen, Karl Øyvind, Soguero-Ruiz, Cristina   +3 more
core   +2 more sources

Feature Extraction of a Planetary Gearbox Based on the KPCA Dual-Kernel Function Optimized by the Swarm Intelligent Fusion Algorithm

Machines
The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction ...
Yan He, Linzheng Ye, Yao Liu
doaj   +1 more source

Fault condition recognition of rolling bearing in bridge crane based on PSO–KPCA

MATEC Web of Conferences, 2017
When the rolling bearing in bridge crane gets out of order and often accompanies with occurrence of nonlinear behaviours, its fault information is weak and it is difficult to extract fault features and to distinguish diverse failure modes.
He Yan, Wang Zongyan
doaj   +1 more source

Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

, 2013
Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher ...
Arenas-García, Jerónimo, Camps-Valls, Gustavo, Hansen, Lars Kai, Petersen, Kaare Brandt   +3 more
core   +1 more source

Fault Diagnosis of an Industrial Chemical Process using Machine Learning Algorithms: Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA)

IOP Conference Series: Materials Science and Engineering
Abstract Fault diagnosis plays a very important role in today’s complex industrial chemical processes. Intelligent fault diagnosis (IFD) is the term for the application of machine learning ideas to the diagnosis of process faults. These past two or three decades have seen a lot of interest in this promising method for releasing the ...
M M Khan, I Islam, A B Rashid
openaire   +1 more source

Nonlinear Regression Estimation Using Subset-Based Kernel Principal Components [PDF]

, 2018
We study the estimation of conditional mean regression functions through the so-called subset-based kernel principal component analysis (KPCA). Instead of using one global kernel feature space, we project a target function into different localized kernel
Ke, Yuan, Li, Degui, Yao, Qiwei
core   +3 more sources

AED-Net: An Abnormal Event Detection Network

Engineering, 2019
It has long been a challenging task to detect an anomaly in a crowded scene. In this paper, a self-supervised framework called the abnormal event detection network (AED-Net), which is composed of a principal component analysis network (PCAnet) and kernel
Tian Wang, Zichen Miao, Yuxin Chen, Yi Zhou, Guangcun Shan, Hichem Snoussi   +5 more
doaj   +1 more source

An Improved Fault Diagnosis Strategy for Process Monitoring Using Reconstruction Based Contributions

IEEE Access, 2021
Air pollution has become the fourth leading cause of premature death on Earth. Air pollution causes poor health and death; about one case out of every ten deaths worldwide is caused by air pollution, which is six times more than malaria. Human activities
Hajer Lahdhiri, Ahamed Aljuhani, Khaoula Ben Abdellafou, Okba Taouali   +3 more
doaj   +1 more source

Stratified Transfer Learning for Cross-domain Activity Recognition

, 2017
In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none ...
Chen, Yiqiang, Hu, Lisha, Peng, Xiaohui, Wang, Jindong, Yu, Philip S.   +4 more
core   +1 more source