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A note on sufficient dimension reduction with post dimension reduction statistical inference
Sufficient dimension reduction is a widely used tool to extract core information hidden in high-dimensional data for classifying, clustering, and...
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A new sufficient dimension reduction method via rank divergence
Sufficient dimension reduction is commonly performed to achieve data reduction and help data visualization. Its main goal is to identify functions of...
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Supervised dimension reduction for functional time series
Functional time series model has been the subject of the most research in recent years, and since functional data is infinite dimensional, dimension...
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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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Sufficient Dimension Reduction and Kernel Dimension Reduction
Suppose there is a dataset that has labels, either for regression or classification. Sufficient Dimension Reduction (SDR), first proposed by Li, is a...
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Maximizing adjusted covariance: new supervised dimension reduction for classification
This study proposes a new linear dimension reduction technique called Maximizing Adjusted Covariance (MAC), which is suitable for supervised...
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Dimension reduction-based adaptive-to-model semi-supervised classification
This paper introduces a novel Dimension Reduction-based Adaptive-to-model Semi-supervised Classification method, specifically designed for scenarios...
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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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Dimension Reduction Based on Sampling
Dimension reduction provides a powerful means of reducing the number of random variables under consideration. However, there were many similar tuples...
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Likelihood-based surrogate dimension reduction
We consider the problem of surrogate sufficient dimension reduction, that is, estimating the central subspace of a regression model, when the...
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Variable-dependent partial dimension reduction
Sufficient dimension reduction reduces the dimension of a regression model without loss of information by replacing the original predictor with its...
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The Performance of a Kernel-Based Variable Dimension Reduction Method
Building forecast models, especially nowcast models, on large data sets of time series variables is a topic of great interest. The most popular...
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Data-driven slicing for dimension reduction in regressions: A likelihood-ratio approach
To efficiently estimate the central subspace in sufficient dimension reduction, response discretization via slicing its range is one of the most used...
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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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High-dimensional local polynomial regression with variable selection and dimension reduction
Variable selection and dimension reduction have been considered in nonparametric regression for improving the precision of estimation, via the...
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Asymptotic results for nonparametric regression estimators after sufficient dimension reduction estimation
Prediction, in regression and classification, is one of the main aims in modern data science. When the number of predictors is large, a common first...
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Similarity-assisted variational autoencoder for nonlinear dimension reduction with application to single-cell RNA sequencing data
BackgroundDeep generative models naturally become nonlinear dimension reduction tools to visualize large-scale datasets such as single-cell RNA...
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A Dynamical System-Based Framework for Dimension Reduction
We propose a novel framework for learning a low-dimensional representation of data based on nonlinear dynamical systems, which we call the dynamical...
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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...
