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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 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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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 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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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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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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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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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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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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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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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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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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Dimension Reduction in the Plate with Tunnel Cuts
We carry out dimension reduction in the homogenization theory 3D periodicity cell problem for the plate with a unidirectional system of channel cuts....
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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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Multi-objective Optimization for Dimension Reduction for Large Datasets
In recent advancement of computational techniques, there is an exponential increase in amount of data. Learning on such large amount of data is a...
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Feature Extraction and Classification of ECG Signals Through Dimension Reduction
Arrhythmia is one of the major heart diseases which causes the death of a large number of people every year. So for the prediction and treatment of...
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Short-Term Power Load Forecasting Model Based on t-SNE Dimension Reduction Visualization Analysis, VMD and LSSVM Improved with Chaotic Sparrow Search Algorithm Optimization
The stable operation of power system has the strong constraint of load balance. Accurate power load forecasting is of great significance in ensuring...
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Dimensionality Reduction Using Band Optimisation
Dimensionality reduction of hyperspectral imagery is an important pre-processing step for achieving improved classification accuracy. In this...
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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...
