Results 21 to 30 of about 29,752 (189)
Kernel-PCA data integration with enhanced interpretability. [PDF]
BMC Syst Biol, 2014 Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-
Reverter F, Vegas E, Oller JM.
europepmc +2 more sources
Applied Sciences, 2022 Fault monitoring is often employed for the secure functioning of industrial systems. To assess performance and enhance product quality, statistical process control (SPC) charts such as Shewhart, CUSUM, and EWMA statistics have historically been utilized.
Faisal Shahzad +2 more
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A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI
Sensors, 2023 Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final ...
Cemre Candemir
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RKF-PCA: Robust kernel fuzzy PCA
Neural Networks, 2009 Principal component analysis (PCA) is a mathematical method that reduces the dimensionality of the data while retaining most of the variation in the data. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components.
Computer and Information Science and Engineering, University of Florida, United States ( host institution ) +3 more
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Journal of Spectroscopy, 2021 To facilitate the enhanced reliability of Raman-based tumor detection and analytical methodologies, an ex vivo Raman spectral investigation was conducted to identify distinct compositional information of healthy (H), ductal carcinoma in situ (DCIS), and ...
Heping Li +7 more
doaj +1 more source
Fault Diagnosis of Industrial Process Based on FDKICA-PCA
Journal of Harbin University of Science and Technology, 2018 Because the dynamic characteristics of autocorrelation and lag correlation in production process are neglected in fault diagnosis,Kernel Independent Component AnalysisPrincipal Component Analysis (KICAPCA) is very poor in detecting small and gradual ...
ZHANG Jing +3 more
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KPCA over PCA to assess urban resilience to floods [PDF]
E3S Web of Conferences, 2021 Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to ...
Satour Narjiss +3 more
doaj +1 more source
An Efficient Data Driven-Based Model for Prediction of the Total Sediment Load in Rivers
Hydrology, 2022 Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ...
Roohollah Noori +9 more
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Invertible Kernel PCA With Random Fourier Features
IEEE Signal Processing Letters, 2023 This work has been submitted to the IEEE for possible ...
Daniel Gedon +3 more
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