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Weining Shen

15 papers hereh-index 13671 citations54 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8
  • last author7

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • stat.ME12
  • stat.AP2
  • stat.ML1
same name
  • Weining Shen — 8 papers, h 6
  • Weining Shen — 4 papers, h 2
  • Weining Shen — 3 papers, h 9
  • Weining Shen — 2 papers, h 1
  • Weining Shen — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172022
most citedGraph signal denoising using t-shrinkage priors

2 citations · 4 across the 8 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

stat.AP2018

Modeling Brain Connectivity with Graphical Models on Frequency Domain

Xu Gao, Weining Shen, Chee-Ming Ting +3

Multichannel electroencephalograms (EEGs) have been widely used to study cortical connectivity during acquisition of motor skills. In this paper, we introduce copula Gaussian graph…

stat.ME2018

Matrix Linear Discriminant Analysis

Wei Hu, Weining Shen, Hua Zhou +1

We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the e…

stat.ME2018

Regularized matrix data clustering and its application to image analysis

Xu Gao, Weining Shen, Hernando Ombao

In this paper, we propose a regularized mixture probabilistic model to cluster matrix data and apply it to brain signals. The approach is able to capture the sparsity (low rank, sm…

stat.ME2018

Generalized probabilistic principal component analysis of correlated data

Mengyang Gu, Weining Shen

Principal component analysis (PCA) is a well-established tool in machine learning and data processing. The principal axes in PCA were shown to be equivalent to the maximum marginal…

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