9 papers
Functional sufficient dimension reduction through information maximization with application to classification
Xinyu Li, Jianjun Xu, Wenquan Cui +1
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) meth…
Federated Covariate Shift Adaptation for Missing Target Output Values
Yaqian Xu, Wenquan Cui, Jianjun Xu +1
The most recent multi-source covariate shift algorithm is an efficient hyperparameter optimization algorithm for missing target output. In this paper, we extend this algorithm to t…
Online Kernel Sliced Inverse Regression
Wenquan Cui, Yue Zhao, Jianjun Xu +1
Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel p…
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Wenquan Cui, Yue Zhao, Jianjun Xu +1
Federated learning has become a popular tool in the big data era nowadays. It trains a centralized model based on data from different clients while keeping data decentralized. In t…
Functional data clustering via information maximization
Xinyu Li, Jianjun Xu, Haoyang Cheng
A new method for clustering functional data is proposed via information maximization. The proposed method learns a probabilistic classifier in an unsupervised manner so that mutual…
Functional sufficient dimension reduction through distance covariance
Xing Yang, Jianjun Xu
Our research proposes a novel method for reducing the dimensionality of functional data, specifically for the case where the response is a scalar and the predictor is a random func…
A new RKHS-based global testing for functional linear model
Jianjun Xu, Wenquan Cui
This article studies global testing of the slope function in functional linear regression model in the framework of reproducing kernel Hilbert space. We propose a new testing stati…
Online Sparse Sliced Inverse Regression
Haoyang Cheng, Wenquan Cui, Xu Jianjun
Due to the demand for tackling the problem of streaming data with high dimensional covariates, we propose an online sparse sliced inverse regression (OSSIR) method for online suffi…
A new reproducing kernel based nonlinear dimension reduction method for survival data
Wenquan Cui, Jianjun Xu, Yuehua Wu
Based on the theories of sliced inverse regression (SIR) and reproducing kernel Hilbert space (RKHS), a new approach RDSIR (RKHS-based Double SIR) to nonlinear dimension reduction…