Andersen M. S. Ang

Andersen M. S. Ang
University of Waterloo | UWaterloo · Department of Combinatorics & Optimization

PhD
UW-Fields Postdoctoral Fellow

About

29
Publications
8,966
Reads
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200
Citations
Additional affiliations
February 2021 - present
University of Waterloo
Position
  • PostDoc Position
Description
  • Postdoc in Department of Combinatorics & Optimization, Faculty of Mathematics. Supervisors: Stephen Vavasis and Hans De Sterck
November 2020 - present
Université de Mons
Position
  • PostDoc Position
August 2019 - September 2019
University of Waterloo
Position
  • Visiting Student
Education
February 2017 - January 2021
Université de Mons
Field of study
  • Continuous Optimisation Algorithms and applications
September 2014 - June 2016
The University of Hong Kong
Field of study
  • Signal Processing and Machine Learning
September 2011 - June 2014
The University of Hong Kong
Field of study
  • Electronic and Communication Engineering

Publications

Publications (29)
Conference Paper
Full-text available
ABSTRACT In recent years, nonnegative matrix factorization (NMF) with volume regularization has been shown to be a powerful identifiable model; for example for hyperspectral unmixing, document classification, community detection and hidden Markov models. In this paper, we show that minimum-volume NMF (min-volNMF) can also be used when the basis matri...
Article
Full-text available
We propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization and from the method of parallel tangents. However, the use of extrapolation in the context of the exact coordinate d...
Preprint
Full-text available
This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an extrapolation strategy in-between block updates, referred to as heuristic extrapolation with restarts (HER). HER significantly accelerates the empirical convergence speed...
Preprint
Full-text available
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 consists in isolating and extracting each of the sources. To perform this task, nonnegative matrix factorization (NMF) based on the Kullback-Leibler and Itakura-Saito $\beta$-d...
Conference Paper
Full-text available
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 elements, we found that a simple algorithm based on Newton's method is able to solve the projection problem to high precision with a complexity roughly about O(n), which has a...
Conference Paper
Full-text available
We introduce a new Nonnegative Matrix Factorization (NMF) model called Nonnegative Unimodal Matrix Factorization (NuMF), which adds on top of NMF the unimodal condition on the columns of thebasis matrix. NuMF finds applications for example in analytical chemistry. We propose a simple but naive brute-force heuristics strategy based on accelerated pr...
Article
This paper is concerned with improving the empirical convergence speed of block‐coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an extrapolation strategy in‐between block updates, referred to as heuristic extrapolation with restarts (HER). HER significantly accelerates the empirical convergence speed...
Preprint
Full-text available
In this paper we consider a Nonnegative Matrix Factorization (NMF) model on complex numbers, in particular, we propose a group complex-NMF (cNMF) model that subsumes the phase-consistency complex NMF for the audio Blind Source Separation (aBSS). Using Wirtinger calculus, we propose a gradient-based algorithm to solve cNMF. The algorithm is then fur...
Preprint
Full-text available
Nonnegative matrix factorization with the minimum-volume criterion (min-vol NMF) guarantees that, under some mild and realistic conditions, the factorization has an essentially unique solution. This result has been successfully leveraged in many applications, including topic modeling, hyperspectral image unmixing, and audio source separation. In th...
Preprint
Full-text available
We introduce a new Nonnegative Matrix Factorization (NMF) model called Nonnegative Unimodal Matrix Factorization (NuMF), which adds on top of NMF the unimodal condition on the columns of the basis matrix. NuMF finds applications for example in analytical chemistry. We propose a simple but naive brute-force heuristics strategy based on accelerated p...
Conference Paper
Full-text available
Tensor decompositions have become a central tool in machine learn-ing to extract interpretable patterns from multiway arrays of data.However, computing the approximate Canonical Polyadic Decom-position (aCPD), one of the most important tensor decompositionmodel, remains a challenge. In this work, we propose several algo-rithms based on extrapolatio...
Article
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 consists in isolating and extracting each of the sources. To perform this task, nonnegative matrix factorization (NMF) based on the Kullback-Leibler and Itakura-Saito β-diverge...
Preprint
Full-text available
Tensor decompositions have become a central tool in machine learning to extract interpretable patterns from multiway arrays of data. However, computing the approximate Canonical Polyadic Decomposition (aCPD), one of the most important tensor decomposition model, remains a challenge. In this work, we propose several algorithms based on extrapolation...
Article
In this paper, 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 efficient algorithms for three different volume regularizers, and compare them on endmember recovery in hyperspectral unmixing. The NMF algorithms developed in this pape...
Preprint
Full-text available
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 efficient algorithms for three different volume regularizers, and compare them on endmember recovery in hyperspectral unmixing. The NMF algorithms developed in this work...
Preprint
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 efficient algorithms for three different volume regularizers, and compare them on endmember recovery in hyperspectral unmixing. The NMF algorithms developed in this work...
Preprint
Full-text available
Abstract Audio source separation concerns techniques used to extract unknown signals called sources from a mixed signal. In this paper, we assume that the audio signal is recorded with a single microphone. Considering a mixed signal composed of various audio sources, the blind audio source separation consists in isolating and extracting each of the...
Preprint
Full-text available
In this work, we consider the problem of approximate Nonnegative Canonical Polyadic Decomposition (aNCPD) of third-order nonnegative tensors, which boils down to minimizing ||T − (U ⊗ V ⊗ W)Ir||_F^2 over element-wise nonnegative matrices U , V and W. We present an accelerated block coordinate descent algorithm that uses Nesterov-like extrapolation...
Conference Paper
Full-text available
This work considers two volume regularized non-negative matrix factorization (NMF) problems that decompose a non-negative matrix X into the product of two nonnegative matrices W and H with a regularization on the volume of the convex hull spanned by the columns of W. This regularizer takes two forms: the determinant (det) and logarithm of the deter...
Preprint
Full-text available
In this paper, we propose a general framework to accelerate significantly the algorithms for non-negative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization and from the method of parallel tangents. However, the use of extrapolation in the context of the tw...
Preprint
Full-text available
In this paper, we propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization and from the method of parallel tangents. However, the use of extrapolation in the context of the two...
Conference Paper
Full-text available
The learning process on Massive Open Online Courses (MOOCs) is learner-driven, where face-to-face interactions are limited. Because of this, instructional videos and forum discussions in MOOCs become critical points of contact. However, in on-campus teaching, some courses (such as philosophy) are often taught in a small-class tutorial setting. How...
Conference Paper
Full-text available
In this paper, a decomposition method is proposed for Separable Non-negative Tensor Factorization (SNTF), which yields a structure similar to the PARATUCK2 model for the decomposition of non-negative tensors. Among many different possibilities for performing tensor factorization, we develop a specific procedure for SNTF with an aim to decompose mul...
Thesis
Full-text available
Non-negative big data arising in many engineering problems may take the form of matrices or multi-dimensional array called tensors. To preserve the non-negativity property for interpretability, these non-negative algebraic structures can only be analyzed by means of Non-negative Matrix Factorization (NMF) and Non-negative Tensor Factorization (NTF)...
Conference Paper
Full-text available
This paper presents a novel wearable single-channel electrooculography (EOG) based human-computer interface (HCI) with a simple system design and robust performance. In the proposed system, EOG signals are generated from double eye blinks, collected by a commercial wearable device (the NeuroSky MindWave headset), and then converted into a sequence...
Article
Full-text available
This brief introduces a new and practical human-machine interface (HMI) system based on single-channel electrooculography (EOG) signals. The proposed system uses a consumer wireless recording device to collect EOG and employs new encoding/decoding paradigms to covey users’ intentions with EOG from eye movements including blinking and looking up. Th...
Thesis
Full-text available
The term human-computer interface (HCI) is generally defined as the communication and interaction between man and machine. Normal HCI devices such as keyboard and mouse are relatively inaccessible and not user-friendly to those locked-in patients who have lost their neuromuscular capabilities. To help such patients, researchers are developing more...

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