Paper Info
Title
Distributed Graph Regularized Non-Negative Matrix Factorization With Greedy Coordinate Descent
Abstract
Graph regularized non-negative matrix factorization (GNMF) decomposes a high-dimensional non-negative data matrix into two low-dimensional matrices with the non-negativity property kept and the geometric structure preserved. Due to its effectiveness, GNMF has been widely used in many fields such as computer vision and data mining. However, GNMF cannot process large-scale datasets on distributed system because the gradient of the graph regularization term costs huge amount of communication overheads among computing nodes. In this paper, we proposed a distributed GNMF (DGNMF) algorithm to overcome this deficiency. Particularly, DGNMF reformulates the graph regularization term to avoid multiplying graph Laplacian by factor matrix through introducing an auxiliary variable and incorporating an equality constraint over it. We optimize DGNMF by using greedy coordinate descent method in the frame of augmented Lagrange method and implement this algorithm on a distributed system. Since DGNMF requires quite few communication overheads among computing nodes, it can be applied to large scale dataset. The preliminary results illustrate efficiency, scalability, and effectiveness of DGNMF.
Year
Venue
Keywords
2016
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Non-negative matrix factorization, graph regularization, big data, coordinate descent, distributed computing
Field
DocType
ISSN
Laplacian matrix,Lagrange multiplier,Computer science,Matrix (mathematics),Matrix decomposition,Theoretical computer science,Artificial intelligence,Non-negative matrix factorization,Linear programming,Coordinate descent,Machine learning,Scalability
Conference
1062-922X
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Ziheng Gao100.34
Naiyang Guan277230.82
Xuhui Huang3289.82
Xuefeng Peng400.34
Zhigang Luo582847.92
Tang Yuhua635.89