Title :
Practical distributed classification using the Alternating Direction Method of Multipliers algorithm
Author :
Lubell-Doughtie, Peter ; Sondag, Jon
Author_Institution :
Intent Media, New York, NY, USA
Abstract :
We describe a specific implementation of the Alternating Direction Method of Multipliers (ADMM) algorithm for distributed optimization. This implementation runs logistic regression with L2 regularization over large datasets and does not require a user-tuned learning rate metaparameter or any tools beyond MapReduce. Throughout we emphasize the practical lessons learned while implementing an iterative MapReduce algorithm and the advantages of remaining within the Hadoop ecosystem.
Keywords :
distributed algorithms; iterative methods; optimisation; pattern classification; regression analysis; Hadoop ecosystem; L2 regularization; alternating direction method of multipliers algorithm; distributed classification; distributed optimization; iterative MapReduce algorithm; logistic regression; Clustering algorithms; Data models; Logistics; Optimization; Prediction algorithms; Predictive models; Vectors; distributed algorithms; distributed computing; optimization; predictive models;
Conference_Titel :
Big Data, 2013 IEEE International Conference on
Conference_Location :
Silicon Valley, CA
DOI :
10.1109/BigData.2013.6691651
