Graph-Based Semi-Supervised Learning With Big Data

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Author(s): Prithish Banerjee (West Virginia University, USA), Mark Vere Culp (West Virginia University, USA), Kenneth Jospeh Ryan (West Virginia University, USA)and George Michailidis (University of Florida, USA)
Copyright: 2017
Pages: 32
Source title: Handbook of Research on Applied Cybernetics and Systems Science
Source Author(s)/Editor(s): Snehanshu Saha (PESIT South Campus, India), Abhyuday Mandal (University of Georgia, USA), Anand Narasimhamurthy (BITS Hyderabad, India), Sarasvathi V (PESIT- Bangalore South Campus, India)and Shivappa Sangam (UGC, India)
DOI: 10.4018/978-1-5225-2498-4.ch007

Keywords: Artificial Intelligence / Cybernetics / Information Science Reference / Media & Communications

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Abstract

This chapter presents some popular graph-based semi-supervised approaches. These techniques apply to classification and regression problems and can be extended to big data problems using recently developed anchor graph enhancements. The background necessary for understanding this Chapter includes linear algebra and optimization. No prior knowledge in methods of machine learning is necessary. An empirical demonstration of the techniques for these methods is also provided on real data set benchmarks.

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