This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …

This paper studies the problem of data-adaptive representations for big, distributed data. It is
assumed that a number of geographically-distributed, interconnected sites have massive …

In this paper, we consider learning dictionary models over a network of agents, where each
agent is only in charge of a portion of the dictionary elements. This formulation is relevant in …

In this paper, we suggest a modified distributed compressed sensing (CS) approach based
on the iterative hard thresholding (IHT) algorithm, namely, distributed IHT (DIHT). Our …

Functional magnetic resonance imaging (fMRI) has provided a window into the brain with
wide adoption in research and even clinical settings. Data-driven methods such as those …

We study a problem in which the nodes of a network, each with different data, are interested
in computing a common dictionary that is suitable for the efficient sparse coding of all their …

Measures of electrodermal activity (EDA) have advanced research in a wide variety of areas
including psychophysiology; however, the majority of this research is typically undertaken in …

Diffusion-based distributed dictionary learning methods are studied in this work. We
consider the classical mixed l 2-l 1 cost function, that employs an l 2 representation error …

We consider the problem of dictionary learning over large scale models, where the model
parameters are distributed over a multi-agent network. We demonstrate that the dual …

This dissertation deals with the development of effective information processing strategies
for distributed optimization and learning over graphs. The work considers initially global cost …