Large scale Machine Learning [closed]

Question

I need to run various machine learning techniques on a big dataset (10-100 billions records) The problems are mostly around text mining/information extraction and include various kernel techniques but are not restricted to them (we use some bayesian methods, bootstrapping, gradient boosting, regression trees -- many different problems and ways to solve them)

What would be the best implementation? I'm experienced in ML but do not have much experience how to do it for huge datasets Is there any extendable and customizable Machine Learning libraries utilizing MapReduce infrastructure Strong preference to c++, but Java and python are ok Amazon Azure or own datacenter (we can afford it)?

Answer 1

Unless the classification state space you are attempting to learn is extremely large, I would expect that there is significant redundancy in a text-mining-focused dataset with 10-100 billion records or training samples. As a rough guess, I would doubt that one would need much more than a 1-2% random sample subset to learn reliable classifiers that would hold up well under cross-validation testing.

A quick literature search came up with the following relevant papers. The Tsang paper claims O(n) time complexity for n training samples, and there is software related to it available as the LibCVM toolkit. The Wolfe paper describes a distributed EM approach based on MapReduce.

Lastly, there was a Large-Scale Machine Learning workshop at the NIPS 2009 conference that looks to have had lots of interesting and relevant presentations.

References

Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung (2005). "Core Vector Machines: Fast SVM Training on Very Large Data Sets", Journal of Machine Learning Research, vol 6, pp 363–392.

J Wolfe, A Haghighi, D Klein (2008). "Fully Distributed EM for Very Large Datasets", Proceedings of the 25th International Conference on Machine Learning, pp 1184-1191.

Olivier Camp, Joaquim B. L. Filipe, Slimane Hammoudi and Mario Piattini (2005). "Mining Very Large Datasets with Support Vector Machine Algorithms ", Enterprise Information Systems V, Springer Netherlands, pp 177-184.