scholarly journals Dimension Reduction Analysis of Vowel Signal Data Based on Manifold Learning

2021 ◽  
Vol 18 ◽  
pp. 148-151
Author(s):  
Jinqing Shen ◽  
Zhongxiao Li ◽  
Xiaodong Zhuang
Keyword(s):  
Dimension Reduction ◽  
Manifold Learning ◽  
Speech Signal ◽  
Reduction Method ◽  
High Dimensional ◽  
Speech Signal Processing ◽  
Dimension Reduction Method ◽  
Important Method ◽  
Signal Features ◽  
Reduction Methods

Data dimension reduction is an important method to overcome dimension disaster and obtain as much valuable information as possible. Speech signal is a kind of non-stationary random signal with high redundancy, and proper dimension reduction methods are needed to extract and analyze the signal features efficiently in speech signal processing. Studies have shown that manifold structure exists in high-dimensional data. Manifold dimension reduction method aiming at discovering the intrinsic geometric structure of data may be more effective in dealing with practical problems. This paper studies a data dimension reduction method based on manifold learning and applies it to the analysis of vowel signals.

A BOOSTED MANIFOLD LEARNING FOR AUTOMATIC FACE RECOGNITION

2010 ◽  
Vol 24 (02) ◽  
pp. 321-335 ◽  
Author(s):  
CHUNYUAN LU ◽  
JIANMIN JIANG ◽  
GUOCAN FENG
Keyword(s):  
Face Recognition ◽  
Manifold Learning ◽  
Reduction Method ◽  
Learning Algorithm ◽  
High Dimensional ◽  
Nonlinear Structures ◽  
Effective Dimension ◽  
Dimension Reduction Method ◽  
Effective Dimension Reduction ◽  
Analysis Computer

Manifold learning is an effective dimension reduction method to extract nonlinear structures from high dimensional data. Recently, manifold learning has received much attention within the research communities of image analysis, computer vision and document data analysis. In this paper, we propose a boosted manifold learning algorithm towards automatic 2D face recognition by using AdaBoost to select the best possible discriminating projection for manifold learning to exploit the strength of both techniques. Experimental results support that the proposed algorithm improves over existing benchmarks in terms of stability and recognition precision rates.

Hybrid multiplicative dimension reduction method for uncertainty analysis of engineering structures

2020 ◽  
pp. 1748006X2092997
Author(s):  
Haihe Li ◽  
Pan Wang ◽  
Qi Chang ◽  
Changcong Zhou ◽  
Zhufeng Yue
Keyword(s):  
Uncertainty Analysis ◽  
Dimension Reduction ◽  
Reduction Method ◽  
High Dimensional ◽  
Sensitivity Index ◽  
Dimension Reduction Method ◽  
Engineering Structures ◽  
High Order Derivative ◽  
Engineering Models ◽  
Complex Engineering

For uncertainty analysis of high-dimensional complex engineering problems, this article proposes a hybrid multiplicative dimension reduction method based on the existent multiplicative dimension reduction method. It uses the multiplicative dimension reduction method to approximate the original high-dimensional performance function which is sufficiently smooth and has a small high-order derivative as the product of a series of one-dimensional functions, and then uses this approximation to calculate the statistical moments of the function. Then the variance-based global sensitivity index is employed to identify the important variables, and the identified important variables are subjected to bivariate decomposition approximation. Combined with the univariate multiplicative dimension reduction method, the hybrid decomposition approximation is obtained. Compared with the existing method, the proposed method is more accurate than the univariate decomposition approximation when used for uncertainty analysis of engineering models and needs less computational efforts than the bivariate decomposition. In the end, a numerical example and two engineering applications are tested to verify the effectiveness of the proposed 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.

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