Results 81 to 90 of about 192,804 (181)

KOMBINASI KPCA DAN EUCLIDEAN DISTANCE UNTUK PENGENALAN CITRA WAJAH

Rekayasa, 2011
Permasalahan machine learning dan pattern recognition bukan merupakan penelitian yang baru. Seiring dengan perkembangan teknologi, semakin berkembang pula teknik dan algoritma yang digunakan untuk menyelesaikan permasalahan machine learning dan pattern ...
Rima Tri Wahyuningrum
doaj   +1 more source

Improved ECG-Derived Respiration Using Empirical Wavelet Transform and Kernel Principal Component Analysis. [PDF]

Comput Intell Neurosci, 2021
Zhuang S, Li F, Zhuang Z, Rao W, Joseph Raj AN, Rajangam V.   +5 more
europepmc   +1 more source

Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy. [PDF]

Front Plant Sci, 2022
Shi G, Shen X, Ren H, Rao Y, Weng S, Tang X.   +5 more
europepmc   +1 more source

Efficient Iterative Dynamic Kernel Principal Component Analysis Monitoring Method for the Batch Process with Super-large-scale Data Sets. [PDF]

ACS Omega, 2021
Wang Y, Yu H, Li X.
europepmc   +1 more source

Statistical skull models from 3D X-ray images

, 2006
We present 2 statistical models of the skull and mandible built upon an elastic registration method of 3D meshes. The aim of this work is to relate degrees of freedom of skull anatomy, as static relations are of main interest for anthropology and legal ...
Bailly, Gérard, Berar, Maxime, Desvignes, Michel, Payan, Yohan   +3 more
core   +1 more source

Feature Extraction of a Planetary Gearbox Based on the KPCA Dual-Kernel Function Optimized by the Swarm Intelligent Fusion Algorithm

Machines
The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction ...
Yan He, Linzheng Ye, Yao Liu
doaj   +1 more source

Kernel Hebbian algorithm for iterative kernel principal component analysis

, 2003
A new method for performing a kernel principal component analysis is proposed. By kernelizing the generalized Hebbian algorithm, one can iteratively estimate the principal components in a reproducing kernel Hilbert space with only linear order memory complexity.
Kim, Kwang In, Franz, Matthias O., Schölkopf, Bernhard   +2 more
openaire   +3 more sources

Arrow Diagrams for Kernel Principal Component Analysis

Communications for Statistical Applications and Methods, 2013
Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis ...
openaire   +2 more sources

On Optimizing Hyperspectral Inversion of Soil Copper Content by Kernel Principal Component Analysis

Remote Sensing
Heavy metal pollution not only causes detrimental effects on the environment but also poses threats to human health; thus, it is crucial to monitor the heavy metal content in the soil.
Fei Guo, Zhen Xu, Honghong Ma, Xiujin Liu, Lei Gao   +4 more
doaj   +1 more source

Deep Kernel Principal Component Analysis for multi-level feature learning

Neural Networks
Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern deep learning tools have achieved great empirical success, but a framework for deep principal component analysis is still lacking.
Tonin, Francesco, Tao, Qinghua, Patrinos, Panagiotis, Suykens, Johan A.K.   +3 more
openaire   +4 more sources