A Non-parametric Dimensionality Reduction Technique Using Gradient Descent of Misclassification Rate

2005 ◽  
pp. 155-164
Author(s):  
S. Redmond ◽  
C. Heneghan
Keyword(s):  
Dimensionality Reduction ◽  
Gradient Descent ◽  
Reduction Technique ◽  
Misclassification Rate ◽  
Dimensionality Reduction Technique ◽  
Non Parametric

scholarly journals Dimensionality Reduction by Weighted Connections between Neighborhoods

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Fuding Xie ◽  
Yutao Fan ◽  
Ming Zhou
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Space ◽  
High Dimensional Data ◽  
Reduction Technique ◽  
Experimental Results ◽  
High Dimensional ◽  
Reduced Dimensionality ◽  
Dimensionality Reduction Technique ◽  
Low Dimensionality ◽  
Local Topology

Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. This paper introduces a dimensionality reduction technique by weighted connections between neighborhoods to improveK-Isomap method, attempting to preserve perfectly the relationships between neighborhoods in the process of dimensionality reduction. The validity of the proposal is tested by three typical examples which are widely employed in the algorithms based on manifold. The experimental results show that the local topology nature of dataset is preserved well while transforming dataset in high-dimensional space into a new dataset in low-dimensionality by the proposed method.

A dimensionality reduction technique for 2D scattering problems in photonics

2010 ◽  
Vol 12 (3) ◽  
pp. 035502 ◽  
Author(s):  
O V (Alyona) Ivanova ◽  
Remco Stoffer ◽  
Manfred Hammer
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Technique ◽  
Scattering Problems ◽  
Dimensionality Reduction Technique

scholarly journals A GAN-based dimensionality reduction technique for aerodynamic shape optimization

2021 ◽  
Author(s):  
Jun Zhang ◽  
Wenzheng Wang ◽  
Qiuyu Wu ◽  
Liwei Hu
Keyword(s):  
Dimensionality Reduction ◽  
Shape Optimization ◽  
Design Space ◽  
Reduction Technique ◽  
Aerodynamic Shape Optimization ◽  
Generative Adversarial Network ◽  
Aerodynamic Shape ◽  
Adversarial Network ◽  
Dimensionality Reduction Technique ◽  
Dynamics Simulations

Abstract Aerodynamic shape optimization (ASO) based on computational fluid dynamics simulations is extremely computationally demanding because a search needs to be performed in a high-dimensional design space. One solution to this problem is to reduce the dimensionality of the design space for aircraft optimization. Hence, in this study, a dimensionality reduction technique is designed based on a generative adversarial network (GAN) to facilitate ASO. The novel GAN model is developed by combining the GAN with airfoil curve parameterization and can directly produce realistic and highly accurate airfoil curves from input data of aerodynamic shapes. In addition, the respective interpretable characteristic airfoil variables can be obtained by extracting latent codes with physical meaning, while reducing the dimensionality of the airfoil design space. The results of simulation experiments show that the proposed technique can significantly improve the optimization convergence rate of the ASO process.

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