Abstract:
Text mining, visual classification, and recommender systems use machine learning to make accurate predictions from data. When processing large datasets, most sophisticated machine learning methods take a long time. Large-scale machine learning (LML) is needed to learn patterns from big data with comparable performance. This paper provides a systematic survey of LML methods to guide future research. First, we divide these LML methods by scalability improvement: model simplification on computational complexities, optimization approximation on computational efficiency, and computation parallelism on computational capabilities. We then categorize each perspective’s methods by scenario and present representative methods using intrinsic strategies. Finally, we discuss their limitations and promising future directions.
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