Peng Wang

most citedOptimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method

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

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math.OC2022

A Linearly Convergent Algorithm for Rotationally Invariant $ℓ_{1}$ -Norm Principal Component Analysis

Taoli Zheng, Peng Wang, Anthony Man-Cho So

To do dimensionality reduction on the datasets with outliers, the $ℓ_{1}$ -norm principal component analysis (L1-PCA) as a typical robust alternative of the conventional PCA has en…

math.OC2021

Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for $ℓ_{1}$ -Norm Principal Component Analysis

Peng Wang, Huikang Liu, Anthony Man-Cho So

A popular robust alternative of the classic principal component analysis (PCA) is the $ℓ_{1}$ -norm PCA (L1-PCA), which aims to find a subspace that captures the most variation in…

math.OC2021★ 3 cited

Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method

Peng Wang, Huikang Liu, Zirui Zhou +1

In this paper, we study the problem of exact community recovery in the symmetric stochastic block model, where a graph of $n$ vertices is randomly generated by partitioning the ver…

A Linearly Convergent Algorithm for Rotationally Invariant ℓ1​-Norm Principal Component Analysis

Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for ℓ1​-Norm Principal Component Analysis

Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method

A Linearly Convergent Algorithm for Rotationally Invariant $ℓ_{1}$ -Norm Principal Component Analysis

Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for $ℓ_{1}$ -Norm Principal Component Analysis