TY - JOUR AU - Gao, Tianxiang AU - Chu, Chris PY - 2018/04/29 Y2 - 2024/03/29 TI - DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v32i1.11736 UR - https://ojs.aaai.org/index.php/AAAI/article/view/11736 SP - AB - <p> 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. </p> ER -