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Cluster Computing
Published in: Cluster Computing 4/2019

01-11-2017

Coordinate descent based ontology sparse vector computing strategy and its applications

Authors: Wei Gao, Muhammad Shoaib Sardar, Sohail Zafar, Zohaib Zahid

Published in: Cluster Computing | Special Issue 4/2019

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Abstract

In recent years, as a semantic analysis and computational tool, ontology has been widely applied in many engineering applications. Many cases suggests that it’s confronted with countless big data source with the complex data structures. In order to relieve the dilemma, the sparse learning algorithms are introduced into the ontology similarity measuring and ontology mapping. In this setting, it should be a high dimensional expression of each ontology vertex, and the ontology algorithm should extract key component information effectively. Under such background, we consider the ontology sparse vector learning algorithm and application in different engineering applications. In this article, by means of coordinate descent minimization tricks, we present the ontology sparse vector optimization strategy and discuss the different transformation in different settings. At last, the new ontology sparse vector learning proceeding is applied to four engineering applications respectively to get its efficiency verified.
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Metadata
Title
Coordinate descent based ontology sparse vector computing strategy and its applications
Authors
Wei Gao
Muhammad Shoaib Sardar
Sohail Zafar
Zohaib Zahid
Publication date
01-11-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 4/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1283-8

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