Results 71 to 80 of about 192,804 (181)
Kernel principal component analysis for multimedia retrieval [PDF]
Global Journal of Information Technology, 2016 Principal component analysis (PCA) is an important tool in many areas including data reduction and interpretation, information retrieval, image processing, and so on. Kernel PCA has recently been proposed as a nonlinear extension of the popular PCA. The basic idea is to first map the input space into a feature space via a nonlinear map and then compute
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IEEE Access, 2019 Kernel-based two-dimensional principal component analysis (K2DPCA), a nonlinear method, was performed to examine ionospheric 2-D total electron content (TEC) variations obtained from the NASA Global Differential GPS (GDGPS) network, which consisted of ...
Jyh-Woei Lin +2 more
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Rainbow plots, Bagplots and Boxplots for Functional Data [PDF]
We propose new tools for visualizing large numbers of functional data in the form of smooth curves or surfaces. The proposed tools include functional versions of the bagplot and boxplot, and make use of the first two robust principal component scores ...
Han Lin Shang, Rob J. Hyndman
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Kernel density weighted principal component analysis of combustion processes
Combustion and Flame, 2012 Abstract Principal component analysis (PCA) has been successfully applied to the analysis of combustion data-sets. However using PCA on a raw direct numerical simulation or an experimental data-set is not straightforward. Indeed, those data-sets usually show non-homogenous data density, hot and cold zones being generally over represented.
Coussement, Axel +2 more
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A novel embedded kernel CNN-PCFF algorithm for breast cancer pathological image classification
Scientific ReportsEarly screening of breast cancer through image recognition technology can significantly increase the survival rate of patients. Therefore, breast cancer pathological image is of great significance for medical diagnosis and clinical research.
Wenbo Liu, Shengnan Liang, Xiwen Qin
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Time Dependent Relative Risk Aversion [PDF]
Risk management and the thorough understanding of the relations between financial markets and the standard theory of macroeconomics have always been among the topics most addressed by researchers, both financial mathematicians and economists.
Enzo Giacomini +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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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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Asian Journal of Research in Computer Science, 2019 Face recognition algorithms can be classified into appearance-based (Linear and Non-Linear Appearance-based) and Model-based Algorithms. Principal Component Analysis (PCA) is an example of Linear Appearance-based which performs a linear dimension reduction while Kernel Principal Component Analysis (KPCA) is an example of non-linear appearance methods ...
Yusuf-Asaju Ayisat Wuraola +2 more
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Kernel principal component analysis (PCA) control chart for monitoring mixed non-linear variable and attribute quality characteristics. [PDF]
Heliyon, 2022 Ahsan M, Mashuri M, Khusna H, Wibawati.
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