9 accepted papers
This paper introduces an efficient multi-linear nonparametric (kernel-based) approximation framework for data regression and imputation. Data features are assumed to reside in or close to a smooth and userunknown manifold embedded in a reproducing kernel Hilbert space. Landmark points are identified…
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 reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approximation properties and inner product of RKHSs, they…
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 density function of the outliers. To this end, an online and data-driven framework…
This work exploits Riemannian manifolds to introduce a geometric framework for online state and community classification in dynamic multilayer networks where nodes are annotated with time series. A bottom-up approach is followed, starting from the extraction of Riemannian features from nodal time se…
This paper introduces a non-parametric learning framework to combat outliers in online, multi-output, and nonlinear regression tasks. A hierarchical-optimization problem underpins the learning task: Search in a reproducing kernel Hilbert space (RKHS) for a function that minimizes a sample average ℓ…
Quadratically constrained quadratic programming (QCQP) forms an important class of optimization tasks in various engineering disciplines. Fast identification of a feasible point under low computational complexity load is critical for several approximation techniques which have been developed to solv…
The hybrid steepest descent method (HSDM) [Yamada, '01] was introduced as a low-computational complexity tool for solving convex variational-inequality problems over the fixed-point set of non-expansive mappings in Hilbert spaces. Motivated by results on decentralized optimization, this study introd…
Partial correlations (PCs) of functional magnetic resonance imaging (fMRI) time series play a principal role in revealing connectivity of brain networks. To explore nonlinear behavior of the blood-oxygen-level dependent signal, the present work postulates a kernel-based nonlinear connectivity model…
This paper advocates a novel framework for segmenting a dataset on a Riemannian manifold M into clusters lying around low-dimensional submanifolds of M. Important examples of M, for which the proposed algorithm is computationally efficient, include the sphere, the set of positive definite matrices,…