no code implementations • 18 May 2023 • Xinyu Li, Jianjun Xu, Wenquan Cui, Haoyang Cheng
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.
no code implementations • 28 Feb 2023 • Yaqian Xu, Wenquan Cui, Jianjun Xu, Haoyang Cheng
The most recent multi-source covariate shift algorithm is an efficient hyperparameter optimization algorithm for missing target output.
no code implementations • 23 Jan 2023 • Wenquan Cui, Yue Zhao, Jianjun Xu, Haoyang Cheng
Federated learning has become a popular tool in the big data era nowadays.
no code implementations • 23 Jan 2023 • Wenquan Cui, Yue Zhao, Jianjun Xu, Haoyang Cheng
Online dimension reduction is a common method for high-dimensional streaming data processing.
no code implementations • ICCV 2023 • Haoyang Cheng, Haitao Wen, Xiaoliang Zhang, Heqian Qiu, Lanxiao Wang, Hongliang Li
In order to address catastrophic forgetting without overfitting on the rehearsal samples, we propose Augmentation Stability Rehearsal (ASR) in this paper, which selects the most representative and discriminative samples by estimating the augmentation stability for rehearsal.
no code implementations • 5 Jan 2021 • Wenquan Cui, Haoyang Cheng
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