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Dimension Reduction
In data science, we are frequently confronted with data sets that have a large number of features. However, many of these features are highly...
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An efficient uncertainty propagation analysis method of non-parameterized P-boxes based on dimension-reduction integral and maximum entropy estimation
The purpose of the non-parameterized P-box uncertainty propagation analysis is to calculate the cumulative distribution function (CDF) bounds of the...
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A subinterval bivariate dimension-reduction method for nonlinear problems with uncertainty parameters
A subinterval bivariate dimension-reduction method is proposed to predict the upper and lower bounds of nonlinear problems with uncertain-but-bounded...
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Structure parameter estimation method for microwave device using dimension reduction network
Gaussian process (GP) is a multi-layer perceptron neural network (NN) with infinite units in its hidden layer that could learn effectively, so as a...
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An approach for reaching consensus in large-scale group decision-making focusing on dimension reduction
Group decision-making and consensus modeling have always been important research topics. With the widespread use of the Internet, group decisions can...
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A novel hybrid dimension reduction and deep learning-based classification for neuromuscular disorder
Correct classification of neuromuscular disorders is essential to provide accurate diagnosis. Presently, gene microarray technology is a widely...
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Manifold-based denoising, outlier detection, and dimension reduction algorithm for high-dimensional data
Manifold learning, which has emerged in recent years, plays an increasingly important role in machine learning. However, because inevitable noises...
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A New Dynamics Analysis Model for Five-Axis Machining of Curved Surface Based on Dimension Reduction and Map**
The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size. Five-axis computer numerical...
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Data-driven method for dimension reduction of nonlinear randomly vibrating systems
Data-driven identification of nonlinear differential equations turns out to be an inefficient, and sometimes even impossible, for high-dimensional...
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Analysis of Deep Generative Model Impact on Feature Extraction and Dimension Reduction for Short Utterance Text-Independent Speaker Verification
Speaker verification is a biometric-based method for individual authentication. However, there are still several challenging problems in achieving...
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Electrical Load Prediction by an Improved Long Short-Term Memory Based on Variable Dimension Reduction
Electrical power load plays an important role in kee** safety and security of power grid and systems. Tremendous attentions have been given in line...
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A hybrid dimensionality reduction method for outlier detection in high-dimensional data
Outlier detection becomes challenging when data are featured by high-dimension. Using dimensionality reduction (DR) techniques to discard the...
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A novel feature dimensionality reduction method for gearbox fault diagnosis with HMSDE, DANCo-DDMA and KELM
This paper proposes a gearbox fault diagnosis method with HMSDE, DANCo-DDMA and AOA-KELM. Firstly, the raw HMSDE features are extracted from the raw...
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Functional Dimension Reduction in Predictive Modeling
AbstractThe paper considers the problem of dimension reduction in metamodeling (predictive modeling). It is shown that to construct an exact...
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A research based on application of dimension reduction technology in data visualization using machine learning
At present, the research work of dimension reduction at home and abroad is more about the theoretical exploration and application research of...
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Nonlinear dimensionality reduction method of scheduling frequent information in wireless networks based on multilevel map**
In order to reduce the redundant data deletion accuracy and bit error rate of wireless network scheduling information, make the reduced dimension...
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Feature Dimensionality Reduction Method on Social Network Dataset
In the social network dataset, excessive data dimensions affect the efficiency of machine learning and analysis of the relationships between data or...
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A context-aware dimension reduction framework for trajectory and health signal analyses
It is practical to collect a huge amount of movement data and environmental context information along with the health signals of individuals because...
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Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis
Concurrent multiscale damage models are often used to quantify the impacts of manufacturing-induced micro-porosity on the damage response of...
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A FEM cluster-based basis reduction method for shakedown analysis of heterogeneous materials
Shakedown analysis with Melan’s theorem is an important approach to predicting the ultimate load-bearing capacity of heterogeneous materials under...
