|dc.description.abstract||Recently, there are increasing reports that most datasets can be actually stored in disks of a single off-the-shelf workstation, and utilizing out-of-core methods is much cheaper and even faster than using a distributed system. For these reasons, out-of-core methods have been actively developed for machine learning and graph processing. The goal of this paper is to develop an efficient out-of-core matrix completion method based on coordinate descent approach. Coordinate descent-based matrix completion (CD-MC) has two strong benefits over other approaches: 1) it does not involve heavy computation such as matrix inversion and 2) it does not have step-size hyper-parameters, which reduces the effort for hyper-parameter tuning. Existing solutions for CD-MC have been developed and analyzed for in-memory setting and they do not take disk-I/O into account. Thus, we propose OCam, a novel out-of-core coordinate descent algorithm for matrix completion. Our evaluation results and cost analyses provide sound evidences supporting the following benefits of OCam: (1) Scalability – OCam is a truly scalable out-of-core method and thus decomposes a matrix larger than the size of memory, (2) Efficiency – OCam is super fast. OCam is up to 10x faster than the state-of-the-art out-of-core method, and up to 4.1x faster than a competing distributed method when using eight machines. The source code of OCam will be available for reproducibility.||-|
|dc.title||OCAM: Out-of-core Coordinate Descent Algorithm for Matrix Completion||-|
|dc.identifier.bibliographicCitation||Information Sciences, v.514||-|
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