Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario
Source abstract
Abstract
The aim behind principal component pursuit is to recover a low-rank matrix and a sparse matrix from a noisy signal which is the sum of both matrices. This optimization problem is a priori and non-convex and is useful in signal processing, data compression, image processing, machine learning, fluid dynamics, and more. Here, a distributed scheme described by a static undirected graph, where each agent only observes part of the noisy or corrupted matrix, is applied to achieve a consensus; then, a robust approach that can also handle missing values is applied using alternating directions to solve the convex relaxation problem, which actually solves the non-convex problem under some weak assumptions. Some examples of image recovery are shown, where the network of agents achieves consensus exponentially fast.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Distributed Control Multi-Agent Systems · Modular Robots and Swarm Intelligence · Neural Networks Stability and Synchronization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Grupo de Investigación e Innovación en Energía, Institución Universitaria Pascual Bravo, Medellín 050034, Colombia
Grupo de Investigación e Innovación Ambiental, Institución Universitaria Pascual Bravo, Medellín 050034, Colombia
Funding
Institución Universitaria Pascual Bravo
References (29)
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