Collaborative mining and transfer learning for relational data

Georgiy Levchuk; Mohammed Eslami

doi:10.1117/12.2177603

18 June 2015 Collaborative mining and transfer learning for relational data

Georgiy Levchuk, Mohammed Eslami

Proceedings Volume 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX; 94940O (2015) https://doi.org/10.1117/12.2177603
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

Many of the real-world problems, – including human knowledge, communication, biological, and cyber network analysis, – deal with data entities for which the essential information is contained in the relations among those entities. Such data must be modeled and analyzed as graphs, with attributes on both objects and relations encode and differentiate their semantics. Traditional data mining algorithms were originally designed for analyzing discrete objects for which a set of features can be defined, and thus cannot be easily adapted to deal with graph data. This gave rise to the relational data mining field of research, of which graph pattern learning is a key sub-domain [11]. In this paper, we describe a model for learning graph patterns in collaborative distributed manner. Distributed pattern learning is challenging due to dependencies between the nodes and relations in the graph, and variability across graph instances. We present three algorithms that trade-off benefits of parallelization and data aggregation, compare their performance to centralized graph learning, and discuss individual benefits and weaknesses of each model. Presented algorithms are designed for linear speedup in distributed computing environments, and learn graph patterns that are both closer to ground truth and provide higher detection rates than centralized mining algorithm.

Citation Download Citation

Georgiy Levchuk and Mohammed Eslami "Collaborative mining and transfer learning for relational data", Proc. SPIE 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, 94940O (18 June 2015); https://doi.org/10.1117/12.2177603

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KEYWORDS

Data modeling

Algorithm development

Mining

Data communications

Image classification

Associative arrays

Biological research

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