Search Results for author:
Found 24 papers, 1 papers with code
Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation
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.
Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications
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
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.
Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering
This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF).
Joint Tensor Factorization and Outlying Slab Suppression with Applications
Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants.
Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches
The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing.
Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related
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.
Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case
In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions.
Enhancing Pure-Pixel Identification Performance via Preconditioning
We analyze robustness of pre-whitening which allows us to characterize situations in which it performs competitively with the SDP-based preconditioning.
Understanding Notions of Stationarity in Non-Smooth Optimization
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.
Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
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.
Minimum Symbol-Error Probability Symbol-Level Precoding with Intelligent Reflecting Surface
Recently, the use of intelligent reflecting surface (IRS) has gained considerable attention in wireless communications.
Probabilistic Simplex Component Analysis
PRISM uses a simple probabilistic model, namely, uniform simplex data distribution and additive Gaussian noise, and it carries out inference by maximum likelihood.
Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation
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 ii) draw connections with some existing SLP designs.
Robust Downlink Transmit Optimization under Quantized Channel Feedback via the Strong Duality for QCQP
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.
SISAL Revisited
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.
Accelerated and Deep Expectation Maximization for One-Bit MIMO-OFDM Detection
In this study we analyze the convergence rate of EM for a class of approximate maximum-likelihood OMOD formulations, or, in a broader sense, a class of problems involving regression from quantized data.
A Spatial Sigma-Delta Approach to Mitigation of Power Amplifier Distortions in Massive MIMO Downlink
A symbol-level precoding (SLP) scheme and a zero-forcing (ZF) precoding scheme, with the new design requirement by the spatial $\Sigma \Delta$ approach being taken into account, are developed.
Spatial Sigma-Delta Modulation for Coarsely Quantized Massive MIMO Downlink: Flexible Designs by Convex Optimization
The challenge with using low-resolution DACs is to overcome the detrimental quantization error effects.
Transmitting Data Through Reconfigurable Intelligent Surface: A Spatial Sigma-Delta Modulation Approach
In this context, we may not be able to apply conventional MIMO precoding schemes, such as the simple zero-forcing (ZF) scheme, and we typically need to design the phase signals by solving optimization problems with constant modulus constraints or with discrete phase constraints, which pose challenges with high computational complexities.
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability
Our multilayer model is based on the postulate that if we arrange the varied endmembers as an expanded endmember matrix, that matrix exhibits a low-rank structure.
Extreme Point Pursuit -- Part II: Further Error Bound Analysis and Applications
In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems.
Extreme Point Pursuit -- Part I: A Framework for Constant Modulus Optimization
This study develops a framework for a class of constant modulus (CM) optimization problems, which covers binary constraints, discrete phase constraints, semi-orthogonal matrix constraints, non-negative semi-orthogonal matrix constraints, and several types of binary assignment constraints.
