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Found 16 papers, 0 papers with code
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering
This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL).
Multilinear Kernel Regression and Imputation via Manifold Learning
This paper introduces a novel nonparametric framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM).
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering
These mappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approximation properties and inner product of RKHSs, they are shown to belong to the powerful Hilbertian family of (firmly) nonexpansive mappings, regardless of the values of their discount factors, and possess ample degrees of design freedom to even reproduce attributes of the classical Bellman mappings and to pave the way for novel RL designs.
Multi-Linear Kernel Regression and Imputation in Data Manifolds
This paper introduces an efficient multi-linear nonparametric (kernel-based) approximation framework for data regression and imputation, and its application to dynamic magnetic-resonance imaging (dMRI).
online and lightweight kernel-based approximated policy iteration for dynamic p-norm linear adaptive filtering
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 the probability density function of the outliers.
Dynamic selection of p-norm in linear adaptive filtering via online kernel-based reinforcement learning
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 the potentially time-varying probability distribution function of the outliers.
Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case
This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem.
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case
This paper introduces a clustering framework for networks with nodes annotated with time-series data.
Robust Hierarchical-Optimization RLS Against Sparse Outliers
This paper fortifies the recently introduced hierarchical-optimization recursive least squares (HO-RLS) against outliers which contaminate infrequently linear-regression models.
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian
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-network clustering via time-series analysis.
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery
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 magnetic-resonance imaging (dMRI).
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case
This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis.
Large-scale subspace clustering using sketching and validation
At the heart of SkeVa-SC lies a randomized scheme for approximating the underlying probability density function of the observed data by kernel smoothing arguments.
Per-Block-Convex Data Modeling by Accelerated Stochastic Approximation
Applications involving dictionary learning, non-negative matrix factorization, subspace clustering, and parallel factor tensor decomposition tasks motivate well algorithms for per-block-convex and non-smooth optimization problems.
Sketch and Validate for Big Data Clustering
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) data.
Riemannian Multi-Manifold Modeling
This paper advocates a novel framework for segmenting a dataset in a Riemannian manifold $M$ into clusters lying around low-dimensional submanifolds of $M$.
