175 citations · 323 across the 6 of their papers we have counts for
6 papers
Multisubject Task-Related fMRI Data Processing via a Two-Stage Generalized Canonical Correlation Analysis
Paris A. Karakasis, Athanasios P. Liavas, Nicholas D. Sidiropoulos +2
Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas…
An Accelerated Stochastic Gradient for Canonical Polyadic Decomposition
Ioanna Siaminou, Athanasios P. Liavas
We consider the problem of structured canonical polyadic decomposition. If the size of the problem is very big, then stochastic gradient approaches are viable alternatives to class…
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation
Ioanna Siaminou, Ioannis Marios Papagiannakos, Christos Kolomvakis +1
We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a stochastic vari…
Design of LDPC Codes for the Unequal Power Two-User Gaussian Multiple Access Channel
Alexios Balatsoukas Stimming, Athanasios P. Liavas
In this work, we describe an LDPC code design framework for the unequal power two-user Gaussian multiple access channel using EXIT charts. We show that the sum-rate of the LDPC cod…
A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization
Kejun Huang, Nicholas D. Sidiropoulos, Athanasios P. Liavas
We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in signal processing and machine learning. The new framework is a h…
Parallel Algorithms for Constrained Tensor Factorization via the Alternating Direction Method of Multipliers
Athanasios P. Liavas, Nicholas D. Sidiropoulos
Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. Wi…