169 citations · 381 across the 21 of their papers we have counts for
22 papers
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering
Yuki Akiyama, Konstantinos Slavakis
This paper aims at the algorithmic/theoretical core of reinforcement learning (RL) by introducing the novel class of proximal Bellman mappings. These mappings are defined in reprod…
Multi-Linear Kernel Regression and Imputation in Data Manifolds
Duc Thien Nguyen, Konstantinos Slavakis
This paper introduces an efficient multi-linear nonparametric (kernel-based) approximation framework for data regression and imputation, and its application to dynamic magnetic-res…
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…
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery
Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose +3
This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magne…
The Stochastic Fejér-Monotone Hybrid Steepest Descent Method and the Hierarchical RLS
Konstantinos Slavakis
This paper introduces the stochastic Fejér-monotone hybrid steepest descent method (S-FM-HSDM) to solve affinely constrained and composite convex minimization tasks. The minimizati…
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case
Konstantinos Slavakis, Shiva Salsabilian, David S. Wack +5
This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis. Time-series data, collected sequentially over time and a…
Fejér-monotone hybrid steepest descent method for affinely constrained and composite convex minimization tasks
Konstantinos Slavakis, Isao Yamada
This paper introduces the Fejér-monotone hybrid steepest descent method (FM-HSDM), a new member to the HSDM family of algorithms, for solving affinely constrained minimization task…
Large-scale subspace clustering using sketching and validation
Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
The nowadays massive amounts of generated and communicated data present major challenges in their processing. While capable of successfully classifying nonlinearly separable object…
Sketch and Validate for Big Data Clustering
Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
In response to the need for learning tools tuned to big data analytics, the present paper introduces a framework for efficient clustering of huge sets of (possibly high-dimensional…
Per-Block-Convex Data Modeling by Accelerated Stochastic Approximation
Konstantinos Slavakis, Georgios B. Giannakis
Applications involving dictionary learning, non-negative matrix factorization, subspace clustering, and parallel factor tensor decomposition tasks motivate well algorithms for per-…
Riemannian Multi-Manifold Modeling
Xu Wang, Konstantinos Slavakis, Gilad Lerman
This paper advocates a novel framework for segmenting a dataset in a Riemannian manifold into clusters lying around low-dimensional submanifolds of . Important examples of $…
Sparsity-Aware Learning and Compressed Sensing: An Overview
Sergios Theodoridis, Yannis Kopsinis, Konstantinos Slavakis
This paper is based on a chapter of a new book on Machine Learning, by the first and third author, which is currently under preparation. We provide an overview of the major theoret…
A Sparsity-Aware Adaptive Algorithm for Distributed Learning
Symeon Chouvardas, Konstantinos Slavakis, Yannis Kopsinis +1
In this paper, a sparsity-aware adaptive algorithm for distributed learning in diffusion networks is developed. The algorithm follows the set-theoretic estimation rationale. At eac…
Generalized Thresholding and Online Sparsity-Aware Learning in a Union of Subspaces
Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis +1
This paper studies a sparse signal recovery task in time-varying (time-adaptive) environments. The contribution of the paper to sparsity-aware online learning is threefold; first,…
Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces
Pantelis Bouboulis, Sergios Theodoridis
The goal of this paper is the development of a novel approach for the problem of Noise Removal, based on the theory of Reproducing Kernels Hilbert Spaces (RKHS). The problem is cas…
The adaptive projected subgradient method constrained by families of quasi-nonexpansive mappings and its application to online learning
Konstantinos Slavakis, Isao Yamada
Many online, i.e., time-adaptive, inverse problems in signal processing and machine learning fall under the wide umbrella of the asymptotic minimization of a sequence of non-negati…
Online Sparse System Identification and Signal Reconstruction using Projections onto Weighted Balls
Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis
This paper presents a novel projection-based adaptive algorithm for sparse signal and system identification. The sequentially observed data are used to generate an equivalent seque…