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Functional sufficient dimension reduction through information maximization with application to classification
Considering the case where the response variable is a categorical variable and the predictor is a random function, two novel functional sufficient dimensional reduction (FSDR) methods are proposed based on mutual information and square loss mutual information.
Federated Covariate Shift Adaptation for Missing Target Output Values
The most recent multi-source covariate shift algorithm is an efficient hyperparameter optimization algorithm for missing target output.
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Federated learning has become a popular tool in the big data era nowadays.
Online Kernel Sliced Inverse Regression
Online dimension reduction is a common method for high-dimensional streaming data processing.
An RKHS-Based Semiparametric Approach to Nonlinear Sufficient Dimension Reduction
By casting the nonlinear dimensional reduction problem in a generalized semiparametric framework, we calculate the orthogonal complement space of generalized nuisance tangent space to derive the estimating equation.
Dimensionality Reduction
Methodology
