Improvement of variables interpretability in kernel PCA [PDF]
BMC Bioinformatics, 2023 Background Kernel methods have been proven to be a powerful tool for the integration and analysis of high-throughput technologies generated data. Kernels offer a nonlinear version of any linear algorithm solely based on dot products.
Mitja Briscik +2 more
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Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA [PDF]
The Scientific World Journal, 2014 Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques.
Zahra Moghaddasi +3 more
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Comparing the performance of Kernel PCA Mix Chart with PCA Mix Chart for monitoring mixed quality characteristics [PDF]
Scientific Reports, 2022 Along with the development of information and technology, the quality characteristics of a product cannot be monitored separately in the different types of control charts.
Muhammad Ahsan +2 more
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Tutorial on PCA and approximate PCA and approximate kernel PCA
Artificial Intelligence Review, 2022 AbstractPrincipal Component Analysis (PCA) is one of the most widely used data analysis methods in machine learning and AI. This manuscript focuses on the mathematical foundation of classical PCA and its application to a small-sample-size scenario and a large dataset in a high-dimensional space scenario.
Sanparith Marukatat +1 more
exaly +2 more sources
Flexible Analog Search with Kernel PCA Embedded Molecule Vectors [PDF]
Computational and Structural Biotechnology Journal, 2017 Studying analog series to find structural transformations that enhance the activity and ADME properties of lead compounds is an important part of drug development.
Stefano Rensi, Russ B. Altman
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Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric [PDF]
Scientific ReportsSharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing.
Amit Pandey +4 more
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The impact of the exponential Kernel’s bandwidth parameter on learning algorithms [PDF]
Scientific ReportsExponential kernels have been used considerably in statistics, machine learning, and artificial intelligence for tasks such as kernel principal component analysis (Kernel PCA), support vector machines(SVM), visualization, clustering, and pattern ...
Mahdi A. Almahdawi
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Comparison of Topic Modelling Approaches in the Banking Context
Applied Sciences, 2023 Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions.
Bayode Ogunleye +4 more
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Autoencoder-PCA-based Online Supervised Feature Extraction-Selection Approach [PDF]
Journal of Artificial Intelligence and Data Mining, 2023 Due to the growing number of data-driven approaches, especially in artificial intelligence and machine learning, extracting appropriate information from the gathered data with the best performance is a remarkable challenge.
Amir Mehrabinezhad +2 more
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Mathematics, 2022 In this study, a multiscale monitoring method for nonlinear processes was developed. We introduced a machine learning tool for fault detection and isolation based on the kernel principal component analysis (PCA) and discrete wavelet transform.
Hanen Chaouch +4 more
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