2016
NIPS
NeurIPS 2016
The Product Cut
Abstract
We introduce a theoretical and algorithmic framework for multi-way graph partitioning that relies on a multiplicative cut-based objective. We refer to this objective as the Product Cut. We provide a detailed investigation of the mathematical properties of this objective and an effective algorithm for its optimization. The proposed model has strong mathematical underpinnings, and the corresponding algorithm achieves state-of-the-art performance on benchmark data sets.
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Interdisciplinary Bridge
— Computer Science and Machine Learning and Mathematics & Optimization
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Trend Setter
— Graph Theory
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Keyword Pioneer
— state-of-the-art performance
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Hot Topic Early Bird
— combinatorial optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio