Distributed Parameter Map-Reduce

TL;DR

This paper introduces a distributed map-reduce approach for logistic regression that efficiently handles large-scale data by distributing both samples and parameters across nodes, enabling scalable training in Hadoop environments.

Contribution

It proposes a novel Distributed Parameter Map-Reduce method that distributes parameters alongside data, facilitating scalable logistic regression training in distributed systems.

Findings

Linear acceleration with increasing cluster nodes

Effective logistic regression training on large datasets

Demonstrated in Hadoop production environment

Abstract

This paper describes how to convert a machine learning problem into a series of map-reduce tasks. We study logistic regression algorithm. In logistic regression algorithm, it is assumed that samples are independent and each sample is assigned a probability. Parameters are obtained by maxmizing the product of all sample probabilities. Rapid expansion of training samples brings challenges to machine learning method. Training samples are so many that they can be only stored in distributed file system and driven by map-reduce style programs. The main step of logistic regression is inference. According to map-reduce spirit, each sample makes inference through a separate map procedure. But the premise of inference is that the map procedure holds parameters for all features in the sample. In this paper, we propose Distributed Parameter Map-Reduce, in which not only samples, but also parameters are distributed in nodes of distributed filesystem. Through a series of map-reduce tasks, we assign each sample parameters for its features, make inference for the sample and update paramters of the model. The above processes are excuted looply until convergence. We test the proposed algorithm in actual hadoop production environment. Experiments show that the acceleration of the algorithm is in linear relationship with the number of cluster nodes.