activity
20192022
collaborators

5 papers

eess.SP2022

Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio

Jaroslav Čmejla, Zbyněk Koldovský, Václav Kautský +1

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time…

q-bio.NC2022

New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning

Fateme Ghayem, Hanlu Yang, Furkan Kantar +3

Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used…

cs.LG2020

Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19

Zois Boukouvalas, Christine Mallinson, Evan Crothers +5

Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating…

stat.ML2019

Multidataset Independent Subspace Analysis with Application to Multimodal Fusion

Rogers F. Silva, Sergey M. Plis, Tulay Adali +2

In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produ…

stat.AP2019

Tracing Network Evolution Using the PARAFAC2 Model

Marie Roald, Suchita Bhinge, Chunying Jia +3

Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatia…