{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T23:35:47Z","timestamp":1723073747451},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, including prototype or graph based. Fundamentally different from the existing combinatorial and spectral solutions, our variational multi-term approach enables to control the trade-off levels between the fairness and clustering objectives. We derive a general tight upper bound based on a concave-convex decomposition of our fairness term, its Lipschitz-gradient property and the Pinsker\u2019s inequality. Our tight upper bound can be jointly optimized with various clustering objectives, while yielding a scalable solution, with convergence guarantee. Interestingly, at each iteration, it performs an independent update for each assignment variable. Therefore, it can be easily distributed for large-scale datasets. This scalability is important as it enables to explore different trade-off levels between the fairness and clustering objectives. Unlike spectral relaxation, our formulation does not require computing its eigenvalue decomposition. We report comprehensive evaluations and comparisons with state-of-the-art methods over various fair clustering benchmarks, which show that our variational formulation can yield highly competitive solutions in terms of fairness and clustering objectives.<\/jats:p>","DOI":"10.1609\/aaai.v35i12.17336","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T19:33:34Z","timestamp":1662665614000},"page":"11202-11209","source":"Crossref","is-referenced-by-count":13,"title":["Variational Fair Clustering"],"prefix":"10.1609","volume":"35","author":[{"given":"Imtiaz Masud","family":"Ziko","sequence":"first","affiliation":[]},{"given":"Jing","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Granger","sequence":"additional","affiliation":[]},{"given":"Ismail","family":"Ben Ayed","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2021,5,18]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/17336\/17143","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/17336\/17143","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T19:33:34Z","timestamp":1662665614000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,18]]},"references-count":0,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,5,28]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v35i12.17336","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2021,5,18]]}}}