11 citations · 17 across the 8 of their papers we have counts for
11 papers
online and lightweight kernel-based approximated policy iteration for dynamic p-norm linear adaptive filtering
Yuki Akiyama, Minh Vu, Konstantinos Slavakis
This paper introduces a solution to the problem of selecting dynamically (online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on th…
Dynamic selection of p-norm in linear adaptive filtering via online kernel-based reinforcement learning
Minh Vu, Yuki Akiyama, Konstantinos Slavakis
This study addresses the problem of selecting dynamically, at each time instance, the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on th…
Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case
Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi +2
This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem. The proposed methodology exploits si…
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case
Cong Ye, Konstantinos Slavakis, Pratik V. Patil +3
This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: State cluste…
Robust Hierarchical-Optimization RLS Against Sparse Outliers
Konstantinos Slavakis, Sinjini Banerjee
This paper fortifies the recently introduced hierarchical-optimization recursive least squares (HO-RLS) against outliers which contaminate infrequently linear-regression models. Ou…
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian
Cong Ye, Konstantinos Slavakis, Pratik V. Patil +2
Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-n…