Results 61 to 70 of about 192,804 (181)

Face Recognition Algorithm Fused Kernel Principal Component Analysis and Minimum Distance Discriminant Projection [PDF]

Jisuanji gongcheng, 2016
By fusing Kernel Principal Component Analysis(KPCA) and Minimum Distance Projection(MDP),a new method based on the original minimum distance differential projection is developed to address the face recognition problem.Different from the classical minimum-
LIU Jun,HUANG Yanqi,XIONG Bangshu
doaj   +1 more source

Quantum machine learning for quantum anomaly detection

, 2017
Anomaly detection is used for identifying data that deviate from `normal' data patterns. Its usage on classical data finds diverse applications in many important areas like fraud detection, medical diagnoses, data cleaning and surveillance.
Liu, Nana, Rebentrost, Patrick
core   +1 more source

A comparative study to examine principal component analysis and kernel principal component analysis-based weighting layer for convolutional neural networks

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
In the recent decay, the focus on processing signal data processing such as time series, images, and videos increased. The purpose of this processing is mainly forecasting, classification, and regression.
Amir Mehrabinezhad, Mohammad Teshnehlab, Arash Sharifi   +2 more
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Nonlinear Multimode Industrial Process Fault Detection Using Modified Kernel Principal Component Analysis

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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Reconocimiento de expresiones faciales utilizando análisis de componentes principales Kernel (KPCA) [PDF]

, 2008
Este artículo presenta una metodología para el reconocimiento de expresiones faciales con análisis de componentes principales kernel, la base de datos utilizada es la Carnegie Mellon University como herramienta de prueba.
Fetecua Valencia, Juan Gabriel, Orozco Gutiérrez, Álvaro Ángel, Álvarez López, Mauricio Alexander   +2 more
core  

Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring

IEEE Access, 2019
Kernel principal component analysis (KPCA) has been widely used for nonlinear process monitoring. However, since the principal components are linear combinations of all kernel functions, traditional KPCA suffers from poor interpretation and high ...
Lingling Guo, Ping Wu, Jinfeng Gao, Siwei Lou   +3 more
doaj   +1 more source

High Dimensional Bayesian Optimization with Kernel Principal Component Analysis

, 2022
Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an acquisition function to suggest candidate points. It is well-known that BO does not scale well for high-dimensional problems because the GPR model requires substantially more ...
Kirill Antonov, Elena Raponi, Hao Wang, Carola Doerr   +3 more
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Research on indoor localization algorithm based on kernel principal component analysis

Tongxin xuebao, 2017
An indoor localization algorithm based on kernel principal component analysis (KPCA) was proposed.It applied KPCA to train the original location fingerprint (OLF) and extract the nonlinear feature of the OLF data at the offline stage,such that the ...
Hua-liang LI, Zhi-hong QIAN, Hong-liang TIAN   +2 more
doaj   +2 more sources

On Software Defect Prediction Using Machine Learning

Journal of Applied Mathematics, 2014
This paper mainly deals with how kernel method can be used for software defect prediction, since the class imbalance can greatly reduce the performance of defect prediction.
Jinsheng Ren, Ke Qin, Ying Ma, Guangchun Luo   +3 more
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HYPERPARAMETER SELECTION IN KERNEL PRINCIPAL COMPONENT ANALYSIS [PDF]

Journal of Computer Science, 2014
In kernel methods, choosing a suitable kernel is in dispensable for favorable results. No well-founded methods, however, have been established in general for unsupervised learning. We focus on kernel Princ ipal Component Analysis (kernel PCA), which is a nonlinear extension of principal component analysis and ha s been used electively for extracting ...
openaire   +1 more source