A Non-Convex Optimization Approach to Correlation Clustering
Paper in proceeding, 2019

We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) framework. We show that the basic approach leads to a simple and natural local search algorithm with guaranteed convergence. This algorithm already beats alternative algorithms by substantial margins in both running time and quality of the clustering. Using ideas from FW algorithms, we develop subsampling and variance reduction paradigms for this approach. This yields both a practical improvement of the algorithm and some interesting further directions to investigate. We demonstrate the performance on both synthetic and real world data sets.

Author

Erik Thiel

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers), Data Science

33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019

5159-5166
978-1-57735-809-1 (ISBN)

33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence
Honolulu, USA,

Subject Categories

Computer and Information Science

ISBN

9781577358091

More information

Latest update

4/21/2023