Results 31 to 40 of about 3,257 (211)

Improved KPCA for supernova photometric classification [PDF]

Proceedings of the International Astronomical Union, 2014
AbstractThe problem of supernova photometric identification is still an open issue faced by large photometric surveys. In a previous investigation, we showed how combining Kernel Principal Component Analysis and Nearest Neighbour algorithms enable us to photometrically classify supernovae with a high rate of success. In the present work, we demonstrate
Ishida, E., de Souza, R., Abdalla, F.
openaire   +2 more sources

KPCA denoising and the pre-image problem revisited

Digital Signal Processing, 2008
Kernel principal component analysis (KPCA) is widely used in classification, feature extraction and denoising applications. In the latter it is unavoidable to deal with the pre-image problem which constitutes the most complex step in the whole processing chain.
Ana R. Teixeira, Ana Maria Tomé, Kurt Stadlthanner, Elmar Wolfgang Lang   +3 more
openaire   +3 more sources

Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys

Materials & Design, 2020
Despite outstanding and unique properties, the structure-property relationship of high entropy alloys (HEAs) is not well established. The machine learning (ML) is used to scrutinize the effect of nine physical quantities on four phases.
Lei Zhang, Hongmei Chen, Xiaoma Tao, Hongguo Cai, Jingneng Liu, Yifang Ouyang, Qing Peng, Yong Du   +7 more
doaj   +1 more source

Auto-KPCA: A Two-Step Hybrid Feature Extraction Technique for Quantitative Structure–Activity Relationship Modeling

IEEE Access, 2021
Quantitative structure-activity relationship (QSAR) modeling is an established approach for drug discovery, but many QSAR datasets suffer from the curse of dimensionality, a challenge that is usually addressed by using dimensionality reduction techniques
Shrooq A. Alsenan, Isra M. Al-Turaiki, Alaaeldin M. Hafez   +2 more
doaj   +1 more source

Application of kernel principal component analysis for optical vector atomic magnetometry

Machine Learning: Science and Technology, 2023
Vector atomic magnetometers that incorporate electromagnetically induced transparency (EIT) allow for precision measurements of magnetic fields that are sensitive to the directionality of the observed field by virtue of fundamental physics.
James A McKelvy, Irina Novikova, Eugeniy E Mikhailov, Mario A Maldonado, Isaac Fan, Yang Li, Ying-Ju Wang, John Kitching, Andrey B Matsko   +8 more
doaj   +1 more source

Fault diagnosis method of track circuit based on KPCA-SAE

Journal of Measurement Science and Instrumentation, 2022
At present, ZPW-2000 track circuit fault diagnosis is artificially analyzed and monitored. Its discrimination method not only is low efficient and takes a long period, but also requires highly experienced personnel to analyze the data.
JIN Zuchen, DONG Yu
doaj  

Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring

IEEE Access, 2019
Kernel principal component analysis (KPCA) has been widely used for nonlinear process monitoring. However, since the principal components are linear combinations of all kernel functions, traditional KPCA suffers from poor interpretation and high ...
Lingling Guo, Ping Wu, Jinfeng Gao, Siwei Lou   +3 more
doaj   +1 more source

Balancing Chain Transfer Agent Transport and Reaction in Emulsion Polymerization

Macromolecular Reaction Engineering, EarlyView.
The interrelationship between chain transfer agent mass transfer and reaction in emulsion polymerization is examined. Three parameters describing these phenomena are proposed. The probability of mass transfer‐ rather than reaction‐limitation for each CTA‐monomer pair is assessed.
F. Joseph Schork
wiley   +1 more source

Tangent Blow‐Ups for Processing Non‐Manifold Geometry

Computer Graphics Forum, EarlyView.
Abstract Many geometry processing pipelines implicitly assume their input data is a manifold, or is sampled from one, with a unique tangent plane at every point. Geometric data, however, routinely contains sharp features like edges, corners, self‐intersections, branching junctions, and other singularities, rendering standard methods ill‐defined at ...
Alice Petrov, Mohammad Sina Nabizadeh, Ana Dodik, Justin Solomon   +3 more
wiley   +1 more source

Speaker Recognition Based on KPCA and KFCM [PDF]

Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering, 2015
Speaker recognition system can identify a certain person using speech analysis. Recent advances in speech processing techniques improve the recognition rate. In this paper, an efficient speaker recognition system is proposed. Firstly, a KPCA-based feature selection approach is adopted to get the efficiently reduced dimension of feature vectors and ...
Yuanyuan Zhang, Jian Wang
openaire   +1 more source