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Feng Yu

4 papers here

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

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • stat.ML2
  • cs.CV1
  • math.OC1
same name
  • Feng Yu — 9 papers, h 13
  • Feng Yu — 4 papers
  • Feng Yu — 2 papers, h 8
  • Feng Yu — 2 papers, h 4
  • Feng Yu — 2 papers, h 3
  • Feng Yu — 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
20192024
most citedRobust Regularized Low-Rank Matrix Models for Regression and Classification

1 citations · 1 across the 2 of their papers we have counts for

collaborators

4 papers

cs.CV2024

A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion

Feng Yu, Teng Zhang, Gilad Lerman

We present the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers. STE is a…

stat.ML2022★ 1 cited

Robust Regularized Low-Rank Matrix Models for Regression and Classification

Hsin-Hsiung Huang, Feng Yu, Xing Fan +1

While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient esti…

stat.ML2021

ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network

Xing Fan, Marianna Pensky, Feng Yu +1

The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped w…

math.OC2019

An Algorithm for Graph-Fused Lasso Based on Graph Decomposition

Feng Yu, Yi Yang, Teng Zhang

This work proposes a new algorithm for solving the graph-fused lasso (GFL), a method for parameter estimation that operates under the assumption that the signal tends to be locally…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.