137 citations · 453 across the 19 of their papers we have counts for
19 papers
Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising
Panagiotis Gkotsis, Athanasios A. Rontogiannis
Deep image prior (DIP) is an unsupervised deep learning framework that has been successfully applied to a variety of inverse imaging problems. However, DIP-based methods are inhere…
Online Rank-Revealing Block-Term Tensor Decomposition
Athanasios A. Rontogiannis, Eleftherios Kofidis, Paris V. Giampouras
The so-called block-term decomposition (BTD) tensor model, especially in its rank- version, has been recently receiving increasing attention due to its enhanced abilit…
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way
Paris V. Giampouras, Athanasios A. Rontogiannis, Eleftherios Kofidis
The so-called block-term decomposition (BTD) tensor model, especially in its rank- version, has been recently receiving increasing attention due to its enhanced abilit…
A novel variational form of the Schatten- quasi-norm
Paris Giampouras, René Vidal, Athanasios Rontogiannis +1
The Schatten- quasi-norm with has recently gained considerable attention in various low-rank matrix estimation problems offering significant benefits over relevant c…
Block-Term Tensor Decomposition: Model Selection and Computation
Athanasios A. Rontogiannis, Eleftherios Kofidis, Paris V. Giampouras
The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are…
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization
Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk…
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in…
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View
Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos E. Themelis +1
Extracting the underlying low-dimensional space where high-dimensional signals often reside has long been at the center of numerous algorithms in the signal processing and machine…
Sparsity-aware Possibilistic Clustering Algorithms
Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
In this paper two novel possibilistic clustering algorithms are presented, which utilize the concept of sparsity. The first one, called sparse possibilistic c-means, exploits spars…
On the convergence of the sparse possibilistic c-means algorithm
Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
In this paper, a convergence proof for the recently proposed sparse possibilistic c-means (SPCM) algorithm is provided, utilizing the celebrated Zangwill convergence theorem. It is…
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing
Paris Giampouras, Konstantinos Themelis, Athanasios Rontogiannis +1
In a plethora of applications dealing with inverse problems, e.g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest…
A Novel Adaptive Possibilistic Clustering Algorithm
Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
In this paper a novel possibilistic c-means clustering algorithm, called Adaptive Possibilistic c-means, is presented. Its main feature is that {\it all} its parameters, after thei…
Semisupervised hyperspectral image unmixing using a variational Bayes algorithm
Konstantinos E. Themelis, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
This technical report presents a variational Bayes algorithm for semisupervised hyperspectral image unmixing. The presented Bayesian model employs a heavy tailed, nonnegatively tru…
Mobile Communication Systems in the Presence of Fading/Shadowing, Noise and Interference
Petros S. Bithas, Athanasios A. Rontogiannis
In this paper, the effects of interference on composite fading environments, where multipath fading coexists with shadowing, are investigated. Based on some mathematical convenient…
A variational Bayes framework for sparse adaptive estimation
Konstantinos E. Themelis, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Recently, a number of mostly -norm regularized least squares type deterministic algorithms have been proposed to address the problem of \emph{sparse} adaptive signal estima…
Approximating the mean of a truncated normal distribution
Konstantinos D. Koutroumbas, Konstantinos E. Themelis, Athanasios A. Rontogiannis
A non trivial problem that arises in several applications is the estimation of the mean of a truncated normal distribution. In this paper, an iterative deterministic scheme for app…
Preamble-based Channel Estimation in OFDM/OQAM Systems: A Review
E. Kofidis, D. Katselis, A. Rontogiannis +1
Filter bank-based multicarrier communications (FBMC) have recently attracted increased interest in both wired (e.g., xDSL, PLC) and wireless (e.g., cognitive radio) applications, d…
On the unmixing of MEx/OMEGA hyperspectral data
Konstantinos E. Themelis, Frédéric Schmidt, Olga Sykioti +3
This article presents a comparative study of three different types of estimators used for supervised linear unmixing of two MEx/OMEGA hyperspectral cubes. The algorithms take into…
Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study
Dimitris Katselis, Eleftherios Kofidis, Athanasios Rontogiannis +1
In this paper, preamble-based least squares (LS) channel estimation in OFDM systems of the QAM and offset QAM (OQAM) types is considered, in both the frequency and the time domains…