DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization


  • Tianxiang Gao Iowa State University
  • Chris Chu Iowa State University




Nonnegative Matrix Factorization, Clustering, Dimension Reduction


Nonnegative matrix factorization (NMF) has attracted much attention in the last decade as a dimension reduction method in many applications. Due to the explosion in the size of data, naturally the samples are collected and stored distributively in local computational nodes. Thus, there is a growing need to develop algorithms in a distributed memory architecture. We propose a novel distributed algorithm, called distributed incremental block coordinate descent (DID), to solve the problem. By adapting the block coordinate descent framework, closed-form update rules are obtained in DID. Moreover, DID performs updates incrementally based on the most recently updated residual matrix. As a result, only one communication step per iteration is required. The correctness, efficiency, and scalability of the proposed algorithm are verified in a series of numerical experiments.




How to Cite

Gao, T., & Chu, C. (2018). DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11736