Solving M-Modes Using Heuristic Search
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3584-3589.
Abstract
M-Modes for graphical models is the problem of finding top M label configurations of highest probability in their local neighborhoods. The state-of-the-art method for solving M-Modes is a dynamic programming algorithm which computes global modes by first computing local modes of each subgraph and then search through all their consistent combinations. A drawback of the algorithm is that most of its time is wasted on computing local modes that are never used in global modes. This paper introduces new algorithms that directly search the space of consistent local modes in finding the global modes, which is enabled by a novel search operator designed to search a subgraph of variables at each time. As a result, the search algorithms only need to generate and verify a small number of local modes and can hence lead to significant improvement in efficiency and scalability.
@inproceedings{ijcai2016p505,
title = {Solving M-Modes Using Heuristic Search},
author = {Chen, Cong and Yuan, Changhe and Chen, Chao},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3584--3589},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/505},
}
TY - CPAPER TI - Solving M-Modes Using Heuristic Search AU - Chen, Cong AU - Yuan, Changhe AU - Chen, Chao T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3584 EP - 3589 UR - https://ijcai.org/proceedings/2016/505 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
