Results 31 to 40 of about 194,949 (275)

Face Recognition Based on Robust Principal Component Analysis and Kernel Sparse Representation [PDF]

Jisuanji gongcheng, 2016
Aiming at the problems that the existing face recognition methods are hard to efficiently overcome the effect of noise and error disturbance (such as illumination,occlusion,and face expression).Kernel sparse representation classification based on Robust ...
LIAO Ruihua,LI Yongfan,LIU Hong
doaj   +1 more source

A novel methodology to create generative statistical models of interconnects [PDF]

, 2016
This paper addresses the problem of constructing a generative statistical model for an interconnect starting from a limited set of S-parameter samples, which are obtained by simulating or measuring the interconnect for a few random realizations of its ...
De Geest, Jan, De Ridder, Simon, De Zutter, Daniël, Dhaene, Tom, Manfredi, Paolo, Vande Ginste, Dries   +5 more
core   +1 more source

Scheduling Dimension Reduction of LPV Models -- A Deep Neural Network Approach [PDF]

, 2020
In this paper, the existing Scheduling Dimension Reduction (SDR) methods for Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network (DNN) approach is developed that achieves higher model accuracy under scheduling dimension reduction.
casella, chollet, goodfellow, hanin, kingma, luo, shamma, srivastava, tóth, tóth   +9 more
core   +2 more sources

Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis

Advances in Electrical and Computer Engineering, 2017
In this paper, based on Kernel Principal Component Analysis (KPCA) of Phasor Measurement Units (PMU) data, a nonlinear method is proposed for fault location in complex power systems.
CHEN, R., SUN, X., LIU, G.
doaj   +1 more source

Submarine Threat Degree Assessment Model Based on Hybrid Kernel Principal Component Analysis [PDF]

Jisuanji gongcheng, 2018
Target threat degree assessment is a crucial link in submarine operations.In order to reduce the complexity of the evaluation and improve the accuracy of evaluation,according to the diversity of the target space sources,the submarine threat degree ...
DONG Xue,ZHANG Deping
doaj   +1 more source

Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction

Energies, 2022
Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered.
Hui Zhang, Cunhua Pan, Yuanxin Wang, Min Xu, Fu Zhou, Xin Yang, Lou Zhu, Chao Zhao, Yangfan Song, Hongwei Chen   +9 more
doaj   +1 more source

Clustering of Gamma-Ray bursts through kernel principal component analysis

, 2017
We consider the problem related to clustering of gamma-ray bursts (from "BATSE" catalogue) through kernel principal component analysis in which our proposed kernel outperforms results of other competent kernels in terms of clustering accuracy and we ...
Chattopadhyay, Asis Kumar, Chattopadhyay, Tanuka, Modak, Soumita   +2 more
core   +1 more source

ECG biometric authentication based on non-fiducial approach using kernel methods [PDF]

, 2016
Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications.
Abdul Aziz, Ahmad Fazli, Hashim, Shaiful Jahari, Hejazi, Maryamsadat, Singh, Yashwant Prasad, Syed Mohamed, Syed Abdul Rahman Al-Haddad   +4 more
core   +1 more source

Distributed Kernel Principal Component Analysis. [PDF]

CoRR, 2015
Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional data collected in a distributed fashion, it becomes very costly to communicate all of this data to a single data center and then perform kernel PCA.
Maria-Florina Balcan, Yingyu Liang, Le Song, David P. Woodruff, Bo Xie 0002   +4 more
openaire   +3 more sources

Statistical Shape Analysis using Kernel PCA [PDF]

, 2006
©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or ...
AD Roy, F Lee, G Jasso, GJ Borjas, GS Maddala, Herbert Brücker, J Schwarze, JJ Heckman, JJ Heckman, JR Harris, L Sjaastad, MC Burda, MC Burda, Parvati Trübswetter, R Willis, RA Nakosteen, S Goldfeld, WH Greene   +17 more
core   +3 more sources