Abstract

Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big Data? Should it depend on the numerical representation of the machine? Since portable embedded systems have been growing in importance, there is also increased interest in implementing machine learning algorithms with a limited number of bits. Not only learning, also feature selection, most of the times a mandatory preprocessing step in machine learning, is often constrained by the available computational resources. In this work, we consider mutual information—one of the most common measures of dependence used in feature selection algorithms—with reduced precision parameters.

Highlights

  • In the age of Big Data, with datasets being collected in almost all fields of human endeavor, there is an emerging economic and scientific need to extract useful information from it

  • For the reduced precision parameters, we compute the number of occurrences and use a lookup table to determine the logarithm of the probability of a particular event

  • Our limited depth mutual information can be applied to any method that uses internally the mutual information measure

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Summary

Introduction

In the age of Big Data, with datasets being collected in almost all fields of human endeavor, there is an emerging economic and scientific need to extract useful information from it. The use of an adequate feature selection method can avoid over-fitting and improve model performance, providing faster and more cost-effective learning models and a deeper insight into the underlying processes that generate the data. The process of feature selection is typically performed on a machine using high numerical representation (64 bits).

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Conclusion

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