A survey of machine learning for big data processing

TLDR

Big data are rapidly expanding across science and engineering, demanding novel learning techniques to fully exploit their potential. This survey reviews recent advances in machine learning for big data processing. The authors survey representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, kernel-based learning, discuss challenges and solutions, explore links to signal processing, and outline open research issues.

Abstract

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.