activity
20182023
most citedBlind Audio Source Separation with Minimum-Volume Beta-Divergence NMF

83 citations · 181 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2023

Inhomogeneous graph trend filtering via a l2,0 cardinality penalty

Xiaoqing Huang, Andersen Ang, Kun Huang +2

We study estimation of piecewise smooth signals over a graph. We propose a -norm penalized Graph Trend Filtering (GTF) model to estimate piecewise smooth graph signals…

math.OC2023★ 2 cited

MGProx: A nonsmooth multigrid proximal gradient method with adaptive restriction for strongly convex optimization

Andersen Ang, Hans De Sterck, Stephen Vavasis

We study the combination of proximal gradient descent with multigrid for solving a class of possibly nonsmooth strongly convex optimization problems. We propose a multigrid proxima…

math.OC2021★ 1 cited

Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics

Andersen Ang, Jianzhu Ma, Nianjun Liu +2

We consider the problem of projecting a vector onto the so-called k-capped simplex, which is a hyper-cube cut by a hyperplane. For an n-dimensional input vector with bounded elemen…

eess.SP2019★ 83 cited

Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF

Valentin Leplat, Nicolas Gillis, Man Shun Ang

Considering a mixed signal composed of various audio sources and recorded with a single microphone, we consider on this paper the blind audio source separation problem which consis…

math.NA2019★ 49 cited

Algorithms and Comparisons of Non-negative Matrix Factorization with Volume Regularization for Hyperspectral Unmixing

M. S. Ang, Nicolas Gillis

In this work, we consider nonnegative matrix factorization (NMF) with a regularization that promotes small volume of the convex hull spanned by the basis matrix. We present highly…

math.NA2018★ 46 cited

Accelerating Nonnegative Matrix Factorization Algorithms using Extrapolation

Andersen Man Shun Ang, Nicolas Gillis

In this paper, we propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolati…