Name
Abstract | ||
|---|---|---|
Nonnegative Matrix Factorization (NMF) has been applied with great success on a wide range of applications. As NMF is increasingly applied to massive datasets such as web-scale dyadic data, it is desirable to leverage a cluster of machines to store those datasets and to speed up the factorization process. However, it is challenging to efficiently implement NMF in a distributed environment. In this... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TKDE.2017.2785326 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Euclidean distance,Loss measurement,Parallel processing,Matrix decomposition,Partitioning algorithms,Convergence | Convergence (routing),Data mining,Distributed Computing Environment,Computer science,Matrix decomposition,Euclidean distance,Theoretical computer science,Non-negative matrix factorization,Factorization,Cloud computing,Speedup | Journal |
Volume | Issue | ISSN |
30 | 6 | 1041-4347 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangtao Yin | 1 | 70 | 6.51 |
| Lixin Gao | 2 | 2898 | 233.01 |
| Zhongfei (Mark) Zhang | 3 | 2451 | 164.30 |