Results 41 to 50 of about 194,949 (275)

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

EURASIP Journal on Advances in Signal Processing, 2009
Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines.
Mathieu Fauvel, Jocelyn Chanussot, Jón Atli Benediktsson   +2 more
doaj   +1 more source

Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network

Journal of Robotics, 2021
In order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary.
Xinning Li, Hu Wu, Xianhai Yang, Peng Xue, Shuai Tan   +4 more
doaj   +1 more source

Fault Diagnosis of Wind Turbine Pitch System Based on Multiblock KPCA Algorithm

IEEE Access, 2021
When the wind turbine pitch system is in operation, due to the strong coupling of the internal structure of the system, it is difficult to accurately locate the fault only relying on prior knowledge.
Wu Yun, Hu Xin
doaj   +1 more source

A novel string representation and kernel function for the comparison of I/O access patterns [PDF]

, 2017
Parallel I/O access patterns act as fingerprints of a parallel program. In order to extract meaningful information from these patterns, they have to be represented appropriately.
B Fryxell, B Schölkopf, G BakIr, J Shawe-Taylor, JM Kunkel, SY Kung, T Gärtner, T Gärtner, T Hastie, TM Madhyastha   +9 more
core   +1 more source

Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins

, 2015
In this paper, we study the structure and dynamical properties of protein contact networks with respect to other biological networks, together with simulated archetypal models acting as probes.
Giuliani, Alessandro, Livi, Lorenzo, Maiorino, Enrico, Pinna, Andrea, Rizzi, Antonello, Sadeghian, Alireza   +5 more
core   +1 more source

Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis [PDF]

, 2012
GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary.
Chen, Feng, Christiani, David C., Diao, Nancy, Lin, Xihong, Zhai, Rihong, Zhao, Yang   +5 more
core   +3 more sources

Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis

Remote Sensing, 2019
Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA ...
Lan Zhang, Hongjun Su, Jingwei Shen
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

Applied Sciences, 2021
The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts.
Elena Quatrini, Francesco Costantino, David Mba, Xiaochuan Li, Tat-Hean Gan   +4 more
doaj   +1 more source

Kernel Mean Shrinkage Estimators [PDF]

, 2016
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern ...
Fukumizu, Kenji, Gretton, Arthur, Muandet, Krikamol, Schölkopf, Bernhard, Sriperumbudur, Bharath   +4 more
core   +2 more sources