Search Results for author: Wing-Kin Ma

Found 17 papers, 1 papers with code

SISAL Revisited

no code implementations1 Jul 2021 Chujun Huang, Mingjie Shao, Wing-Kin Ma, Anthony Man-Cho So

By establishing associations between the SISAL algorithm and a line-search-based proximal gradient method, we confirm that SISAL can indeed guarantee convergence to a stationary point.

On Hyperspectral Unmixing

no code implementations27 Jun 2021 Wing-Kin Ma

The development of DECA shows foresight years ahead, in that regard.

Hyperspectral Unmixing

Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation

no code implementations30 Mar 2021 Yatao Liu, Mingjie Shao, Wing-Kin Ma, Qiang Li

We examine how insights arising from this perturbed ZF and VP interpretations can be leveraged to i) substantially simplify the optimization of certain SLP design criteria, namely, total or peak power minimization subject to SEP quality guarantees and under quadrature amplitude modulation (QAM) constellations; and ii) derive heuristic but computationally cheaper SLP designs.

Probabilistic Simplex Component Analysis

no code implementations18 Mar 2021 Ruiyuan Wu, Wing-Kin Ma, Yuening Li, Anthony Man-Cho So, Nicholas D. Sidiropoulos

PRISM uses a simple probabilistic model, namely, uniform simplex data distribution and additive Gaussian noise, and it carries out inference by maximum likelihood.

Hyperspectral Unmixing Variational Inference

Robust Downlink Transmit Optimization under Quantized Channel Feedback via the Strong Duality for QCQP

no code implementations14 Dec 2020 Xianming Li, Yongwei Huang, Wing-Kin Ma

The minimization problem is accordingly turned into a QMI problem, and the problem is solved by a restricted linear matrix inequality relaxation with additional valid convex constraints.


Understanding Notions of Stationarity in Non-Smooth Optimization

no code implementations26 Jun 2020 Jiajin Li, Anthony Man-Cho So, Wing-Kin Ma

Many contemporary applications in signal processing and machine learning give rise to structured non-convex non-smooth optimization problems that can often be tackled by simple iterative methods quite effectively.

Minimum Symbol-Error Probability Symbol-Level Precoding with Intelligent Reflecting Surface

no code implementations19 Jan 2020 Mingjie Shao, Qiang Li, Wing-Kin Ma

Recently, the use of intelligent reflecting surface (IRS) has gained considerable attention in wireless communications.

Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation

1 code implementation2 Jul 2019 Ruiyuan Wu, Wing-Kin Ma, Xiao Fu, Qiang Li

Hyperspectral super-resolution (HSR) is a problem that aims to estimate an image of high spectral and spatial resolutions from a pair of co-registered multispectral (MS) and hyperspectral (HS) images, which have coarser spectral and spatial resolutions, respectively.


Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach

no code implementations15 Apr 2018 Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos, Wing-Kin Ma

Third, the majority of the existing methods assume that there are known (or easily estimated) degradation operators applied to the SRI to form the corresponding HSI and MSI--which is hardly the case in practice.


Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications

no code implementations3 Mar 2018 Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos, Wing-Kin Ma

Perhaps a bit surprisingly, the understanding to its model identifiability---the major reason behind the interpretability in many applications such as topic mining and hyperspectral imaging---had been rather limited until recent years.

Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization

no code implementations9 Aug 2017 Chia-Hsiang Lin, Ruiyuan Wu, Wing-Kin Ma, Chong-Yung Chi, Yue Wang

This maximum volume inscribed ellipsoid (MVIE) idea has not been attempted in prior literature, and we show a sufficient condition under which the MVIE framework guarantees exact recovery of the factors.

Hyperspectral Unmixing

Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering

no code implementations15 Aug 2016 Xiao Fu, Kejun Huang, Bo Yang, Wing-Kin Ma, Nicholas D. Sidiropoulos

This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF).

Joint Tensor Factorization and Outlying Slab Suppression with Applications

no code implementations16 Jul 2015 Xiao Fu, Kejun Huang, Wing-Kin Ma, Nicholas D. Sidiropoulos, Rasmus Bro

Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants.

Speech Separation

Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches

no code implementations7 Jul 2015 Xiao Fu, Wing-Kin Ma, José Bioucas-Dias, Tsung-Han Chan

The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing.

Hyperspectral Unmixing

Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related

no code implementations15 Sep 2014 Xiao Fu, Wing-Kin Ma, Tsung-Han Chan, José M. Bioucas-Dias

We then perform exact recovery analyses, and prove that the proposed greedy algorithm is robust to noise---including its identification of the (unknown) number of endmembers---under a sufficiently low noise level.

Hyperspectral Unmixing

Enhancing Pure-Pixel Identification Performance via Preconditioning

no code implementations20 Jun 2014 Nicolas Gillis, Wing-Kin Ma

We analyze robustness of pre-whitening which allows us to characterize situations in which it performs competitively with the SDP-based preconditioning.

Hyperspectral Unmixing

Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case

no code implementations20 Jun 2014 Chia-Hsiang Lin, Wing-Kin Ma, Wei-Chiang Li, Chong-Yung Chi, ArulMurugan Ambikapathi

In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions.

Hyperspectral Unmixing

Cannot find the paper you are looking for? You can Submit a new open access paper.