Results 21 to 30 of about 26,356,550 (281)

Comprehensive evaluation of robotic global performance based on modified principal component analysis

International Journal of Advanced Robotic Systems, 2020
The multivariate statistical method such as principal component analysis based on linear dimension reduction and kernel principal component analysis based on nonlinear dimension reduction as the modified principal component analysis method are commonly ...
Liming Li, Jing Zhao, Chunrong Wang, Chaojie Yan   +3 more
doaj   +1 more source

HYPERPARAMETER SELECTION IN KERNEL PRINCIPAL COMPONENT ANALYSIS [PDF]

Journal of Computer Science, 2014
In kernel methods, choosing a suitable kernel is in dispensable for favorable results. No well-founded methods, however, have been established in general for unsupervised learning. We focus on kernel Princ ipal Component Analysis (kernel PCA), which is a nonlinear extension of principal component analysis and ha s been used electively for extracting ...
Md. Ashad Alam, Kenji Fukumizu
openaire   +2 more sources

Research on Rolling Bearing Fault Diagnosis Based on Volterra Kernel Identification and KPCA

Shock and Vibration, 2023
A rolling bearing fault diagnosis method based on the Volterra series and kernel principal component analysis (KPCA) is proposed. In the proposed method, first, the improved genetic algorithm (IGA) is used to identify the Volterra series model of the ...
Yahui Wang, Rong Dong, Xinchao Wang, Xunying Zhang   +3 more
doaj   +1 more source

Pengenalan Jenis Kelamin Berbasis Kernel Principal Component Analysis

Rekayasa, 2015
Gender Recognition  adalah salah satu penelitian di bidang biometrik dan computer vision yang cukup popular. Gender Recognition adalah pengembangan dari Face Recognition, Gender Recognition dapat mengklasifikasikan citra menjadi 2 kelas yaitu perempuan ...
Achmad Rizal
doaj   +1 more source

Structural components in functional data. [PDF]

, 2009
Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space.
Gasser, Theo, Rousson, Valentin, Park, Juhyun   +2 more
core   +5 more sources

Kernel principal component analysis of the ear morphology [PDF]

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
This paper describes features in the ear shape that change across a population of ears and explores the corresponding changes in ear acoustics. The statistical analysis conducted over the space of ear shapes uses a kernel principal component analysis (KPCA).
Reza Zolfaghari, Nicolas Epain, Craig T. Jin, Joan Alexis Glaunès, Anthony I. Tew   +4 more
openaire   +3 more sources

Image fusion for dynamic contrast enhanced magnetic resonance imaging [PDF]

, 2004
Twellmann T, Saalbach A, Gerstung O, Leach MO, Nattkemper TW. Image fusion for dynamic contrast enhanced magnetic resonance imaging. Biomed Eng Online. 2004;3(1): 35.BACKGROUND: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic ...
Nattkemper, Tim Wilhelm ; https://orcid.org/, Nattkemper Tim W, Saalbach, A, Olaf Gerstung, Gerstung, O., Leach, M.O., Twellmann Thorsten, Twellmann, Thorsten, Nattkemper, TW, Axel Saalbach, Tim W Nattkemper, Leach, Martin O, Gerstung Olaf, Twellmann, T Thorsten, Thorsten Twellmann, Gerstung, Olaf, Saalbach Axel, Saalbach, Axel, Leach Martin O, Gerstung, O, Nattkemper, T.W., Martin O Leach, Saalbach, A., Twellmann, T.   +23 more
core   +2 more sources

Streaming Kernel Principal Component Analysis

CoRR, 2015
Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time ...
Mina Ghashami, Daniel J. Perry, Jeff M. Phillips   +2 more
openaire   +3 more sources

State-space independent component analysis for nonlinear dynamic process monitoring [PDF]

, 2010
The cost effective benefits of process monitoring will never be over emphasised. Amongst monitoring techniques, the Independent Component Analysis (ICA) is an efficient tool to reveal hidden factors from process measurements, which follow non-Gaussian
Odiowei, P. P., Cao, Yi
core   +1 more source

Kernel Hebbian algorithm for iterative kernel principal component analysis [PDF]

, 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 ...
Kim, Kwang In, Franz, Matthias O., Schölkopf, Bernhard   +2 more
core   +4 more sources