Results 21 to 30 of about 3,257 (211)
KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion [PDF]
Jisuanji kexue, 2022 Novelty detection is an important research issue in the field of machine learning.Till now,there exist lots of novelty detection approaches.As a commonly used kernel method,kernel principal component analysis(KPCA)has been successfully applied to deal ...
LI Qi-ye, XING Hong-jie
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Nonlinear chemical processes fault detection based on adaptive kernel principal component analysis
Systems Science & Control Engineering, 2020 When kernel Principal Component Analysis (KPCA) is applied to fault detection, kernel Principal Components (KPCs) are divided into two spaces according to the size of variance for fault detection, respectively.
Chen Miao, Zhaomin Lv
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Applied Sciences, 2021 Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of ...
Yingying Fan +4 more
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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
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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
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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
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KPCA and ELM ensemble modeling of wastewater effluent quality indices [PDF]
, 2011 Reliable measurements of effluent quality are important for different operational tasks such as process monitoring, online simulation, and advanced control in the wastewater treatment process (WWTP).
Yuan, D.C. +7 more
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Visualization of principal components based on KPCA feature Extraction.
, 2020 Visualization of principal components based on KPCA feature Extraction.
Lerui Chen (8399430) +2 more
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Robust Kernel Principal Component Analysis With ℓ2,1-Regularized Loss Minimization
IEEE Access, 2020 Principal component analysis (PCA) is a widely used unsupervised method for dimensionality reduction. The kernelized version is called kernel principal component analysis (KPCA), which can capture the nonlinear data structure.
Duo Wang, Toshihisa Tanaka
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International Journal of Computational Intelligence Systems, 2021 Diagnosing incipient faults of rotating machines is very important for reducing economic losses and avoiding accidents caused by faults. However, diagnoses of locations and sizes of incipient faults are very difficult in a noisy background. In this paper,
Heli Wang +3 more
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