Results 131 to 140 of about 4,584 (156)

Classification and de-noising of communication signals using kernel principal component analysis (KPCA)

IEEE International Conference on Acoustics Speech and Signal Processing, 2002
This paper is concerned with the classification and de-noising problem for non-linear signals. It is known that using kernel functions, a non-linear signal can be transformed into a linear signal in a higher dimensional space. In that feature space, a linear algorithm can be applied to a non-linear problem.
null Koutsogiannis, null Soraghan
openaire   +2 more sources

Adaptive Kernel Principal Component Analysis (KPCA) for Monitoring Small Disturbances of Nonlinear Processes

Industrial & Engineering Chemistry Research, 2010
The Tennessee Eastman (TE) process, created by Eastman Chemical Company, is a complex nonlinear process. Many previous studies focus on the detectability of monitoring a multivariate process by using TE process as an example. Principal component analysis (PCA) is a widely used dimension-reduction tool for monitoring multivariate linear process ...
Chun-Yuan Cheng, Chun-Chin Hsu, Mu-Chen Chen   +2 more
openaire   +1 more source

Accelerating kernel principal component analysis (KPCA) by utilizing two‐dimensional wavelet compression: applications to spectroscopic imaging

Journal of Chemometrics, 2008
AbstractPrincipal component analysis (PCA) is a standard tool for analyzing spectroscopic data. However, PCA can at most discriminate a number of spectroscopic signatures that is either equal to the number of variables or to the number of samples, whichever is smaller. Furthermore, linear algorithms are not well adapted to model nonlinear relationships
Robert D. Luttrell, Frank Vogt
openaire   +1 more source

Kernel Principal Component Analysis (KPCA)-Based Face Recognition

2013
As a sub-field of pattern recognition, face recognition (or face classification) has become a hot research point. In pattern recognition and in image processing, feature extraction based no dimensionality reduction plays the important role in the relative areas.
Jun-Bao Li, Shu-Chuan Chu, Jeng-Shyang Pan   +2 more
openaire   +1 more source

Kernel principal component analysis KPCA) in electrical facies classification

2023
Okhovvat, Hamid Reza, Riahi, Mohammad Ali, Akbari Dehkharghani, Afshin   +2 more
openaire   +1 more source

Source Term Inversion Coupling Kernel Principal Component Analysis, Whale Optimization Algorithm, and Backpropagation Neural Networks (Kpca-Woa-Bpnn) for Complex Dispersion Scenarios

Progress in Nuclear Energy, 2023
Xinpeng Li, Jiayue Song, Li Yang, Huanting Li, Sheng Fang   +4 more
openaire   +1 more source

СРАВНИТЕЛНО РАЗПОЗАВАНЕ НА ЛИЦА СЪС СЕЛЕКТИРАНИ РЕГИОНИ С PRINCIPAL COMPONENT ANALYSIS (PCA) И KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA) И ФИЛТРИ НА ГАБОР

2017
В статията се разпознават лица чрез техниките Principal Component Analysis (PCA) и Kernel Principal Component analysis (KPCA) с използване на филтрите на Габор при не идеални условия. С двата метода паралелно се селектират различни по големина региони на интерес - Regions of Interest (ROI's) от всяко изображение в базата данни и се разпознават лица ...
openaire   +1 more source

Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

IEEE Transactions on Cybernetics, 2022
Chen Wu, BO DU, Liangpei Zhang
exaly  

KPCA-CCA-Based Quality-Related Fault Detection and Diagnosis Method for Nonlinear Process Monitoring

IEEE Transactions on Industrial Informatics, 2023
Guang Wang, jinghui yang, Jianfang Jiao
exaly  

Learning a data-dependent kernel function for KPCA-based nonlinear process monitoring

Chemical Engineering Research and Design, 2009
Jong Min Lee
exaly