Results 51 to 60 of about 4,584 (156)
Dimensionality Reduction Nonlinear Partial Least Squares Method for Quality-Oriented Fault Detection
MathematicsUnlike traditional fault detection methods, quality-oriented fault detection further classifies the types of faults into quality-related and non-quality-related faults.
Jie Yuan, Hao Ma, Yan Wang
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Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis
Advances in Electrical and Computer Engineering, 2017 In this paper, based on Kernel Principal Component Analysis (KPCA) of Phasor Measurement Units (PMU) data, a nonlinear method is proposed for fault location in complex power systems.
CHEN, R., SUN, X., LIU, G.
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Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering
Journal of Telecommunications and Information Technology, 2015 The presented paper is concerned with feature space derivation through feature selection. The selection is performed on results of kernel Principal Component Analysis (kPCA) of input data samples.
Krzysztof Ślot +3 more
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, 2014 We consider the image classification problem via kernel collaborative representation classification with locality constrained dictionary (KCRC-LCD). Specifically, we propose a kernel collaborative representation classification (KCRC) approach in which ...
Li, Hui +5 more
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, 2017 New social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently,
Camps-Valls, Gustau +5 more
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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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Jurnal Ilmu Dasar, 2010 Principal component analysis (PCA) is a method used to reduce dimentionality of the dataset. However, the use of PCA failed to carry out the problem of non-linear and non-separable data.
Ismail Djakaria +2 more
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Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap
Sensors, 2011 Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed ...
Xiaoming Zhao, Shiqing Zhang
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Facial Expression Recognition using kernel principal component analysis (KPCA)
, 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. El método utiliza una función kernel que mapea los datos del espacio característico original a uno de mayor dimensionalidad, de ...
Orozco Gutiérrez, Álvaro Ángel +2 more
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Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off
, 2018 Kernel methods are powerful learning methodologies that provide a simple way to construct nonlinear algorithms from linear ones. Despite their popularity, they suffer from poor scalability in big data scenarios.
Sriperumbudur, Bharath, Sterge, Nicholas
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