Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ins.2019.09.077 | Information Sciences |
Keywords | Field | DocType |
Matrix completion,Out-of-core method,Coordinate descent | Matrix completion,Source code,Matrix (mathematics),Parallel computing,Workstation,Out-of-core algorithm,Artificial intelligence,Coordinate descent,Mathematics,Machine learning,Computation,Scalability | Journal |
Volume | ISSN | Citations |
514 | 0020-0255 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongha Lee | 1 | 14 | 6.77 |
| Jinoh Oh | 2 | 303 | 15.32 |
| Hwanjo Yu | 3 | 1715 | 114.02 |