Induced Dimension Reduction Method to Solve the Quadratic Eigenvalue Problem

2017 ◽  
pp. 203-211
Author(s):  
R. Astudillo ◽  
M. B. van Gijzen
Keyword(s):  
Eigenvalue Problem ◽  
Dimension Reduction ◽  
Reduction Method ◽  
Dimension Reduction Method ◽  
Quadratic Eigenvalue Problem ◽  
Quadratic Eigenvalue

Approximation of damped quadratic eigenvalue problem by dimension reduction

2019 ◽  
Vol 347 ◽  
pp. 40-53 ◽  
Author(s):  
Ninoslav Truhar ◽  
Zoran Tomljanović ◽  
Matea Puvača
Keyword(s):  
Eigenvalue Problem ◽  
Dimension Reduction ◽  
Quadratic Eigenvalue Problem ◽  
Quadratic Eigenvalue

Dimension reduction method for reliability-based robust design optimization

2008 ◽  
Vol 86 (13-14) ◽  
pp. 1550-1562 ◽  
Author(s):  
Ikjin Lee ◽  
K.K. Choi ◽  
Liu Du ◽  
David Gorsich
Keyword(s):  
Dimension Reduction ◽  
Design Optimization ◽  
Robust Design ◽  
Reduction Method ◽  
Robust Design Optimization ◽  
Dimension Reduction Method

scholarly journals Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Zhao ◽  
Satish V. Ukkusuri ◽  
Jian Lu
Keyword(s):  
Dimension Reduction ◽  
Traffic Flow ◽  
Reduction Method ◽  
Prediction Models ◽  
Urban Traffic ◽  
Dimension Reduction Method ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.

scholarly journals QUADRO: A supervised dimension reduction method via Rayleigh quotient optimization

2015 ◽  
Vol 43 (4) ◽  
pp. 1498-1534 ◽  
Author(s):  
Jianqing Fan ◽  
Zheng Tracy Ke ◽  
Han Liu ◽  
Lucy Xia
Keyword(s):  
Dimension Reduction ◽  
Reduction Method ◽  
Rayleigh Quotient ◽  
Dimension Reduction Method ◽  
Supervised Dimension Reduction
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