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Large-Scale Matrix Completion Using Orthogonal Rank-One Matrix Pursuit, Divide-Factor-Combine, and Apache Spark


Abstract As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a new generation of Big Data tools to address the shortcomings of the previous generation. One important example of this is the improved performance in the newer t... (more)
Created Date 2014
Contributor Krouse, Brian Richard (Author) / Ye, Jieping (Advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Subject Computer science / Artificial intelligence / Big Data / Hadoop / Machine Learning / Mahout / Matrix Completion / Spark
Type Masters Thesis
Extent 51 pages
Language English
Rights All Rights Reserved
Note M.S. Computer Science 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis