Optimistic Concurrency Control for Distributed Unsupervised Learning

Autor:	Pan, X., Gonzalez, J., Jegelka, S., Broderick, T., Michael Jordan
Jazyk:	angličtina
Rok vydání:	2013
Předmět:	FOS: Computer and information sciences Computer Science - Learning Artificial Intelligence (cs.AI) Computer Science - Distributed Parallel and Cluster Computing Computer Science - Artificial Intelligence Distributed Parallel and Cluster Computing (cs.DC) Machine Learning (cs.LG)
Zdroj:	Scopus-Elsevier
Popis:	Research on distributed machine learning algorithms has focused primarily on one of two extremes - algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints. We consider an intermediate alternative in which algorithms optimistically assume that conflicts are unlikely and if conflicts do arise a conflict-resolution protocol is invoked. We view this "optimistic concurrency control" paradigm as particularly appropriate for large-scale machine learning algorithms, particularly in the unsupervised setting. We demonstrate our approach in three problem areas: clustering, feature learning and online facility location. We evaluate our methods via large-scale experiments in a cluster computing environment. 25 pages, 5 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70c1eea2f80423ed84352c10aa749ef3 http://arxiv.org/abs/1307.8049 Zobrazit plný text záznamu