Learning from Large Distributed Data: A Scaling Down Sampling Scheme for Efficient Data Processing

Provided by: International Association of Computer Science and Information Technology(IACSIT)
Topic: Big Data
Format: PDF
Extracting information from a training data set for predictive inference is a fundamental task in data mining and machine learning. With the exponential growth in the amount of data being generated in the past few years, there is an urgent need to develop or adapt existing learning algorithms to efficiently learn from large data sets. This paper describes three scaling techniques enabling machine learning algorithms to learn from large distributed data sets. First, a general single-pass formula for computing the covariance matrix of large data sets using the MapReduce framework is derived.

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