Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Cheng, Haoyang"'
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 in
Externí odkaz:
http://arxiv.org/abs/2305.10880
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 the framework of federated learning. For data islands in federated lear
Externí odkaz:
http://arxiv.org/abs/2302.14427
Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other methods have been studied in dep
Externí odkaz:
http://arxiv.org/abs/2301.09516
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 this paper, we propose a federated sparse sliced inverse regression alg
Externí odkaz:
http://arxiv.org/abs/2301.09500
Publikováno v:
Chin. J. Chem. Phys. 36(6), 685-690 (2023)
Electronic correlation is a fundamental topic in many-electron systems. To characterize this correlation, one may introduce the concept of exchange-correlation hole. In this paper, we first briefly revisit its definition and relation to electron and
Externí odkaz:
http://arxiv.org/abs/2210.14475
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 information (or squared loss mutual information) between data points
Externí odkaz:
http://arxiv.org/abs/2210.10554
Autor:
Cui, Wenquan, Cheng, Haoyang
Based on the theory of reproducing kernel Hilbert space (RKHS) and semiparametric method, we propose a new approach to nonlinear dimension reduction. The method extends the semiparametric method into a more generalized domain where both the intereste
Externí odkaz:
http://arxiv.org/abs/2101.01535
The distribution profiles of tetracycline resistance genes in rice: Comparisons using four genotypes
Publikováno v:
In Science of the Total Environment 15 January 2024 908
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 sufficient dimension reduction. The existing online sufficient dimension re
Externí odkaz:
http://arxiv.org/abs/2009.14615
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