Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric. (arXiv:2209.12396v2 [cs.LG] UPDATED)

Fair clustering aims to divide data into distinct clusters while preventing
sensitive attributes (\textit{e.g.}, gender, race, RNA sequencing technique)
from dominating the clustering. Although a number of works have been conducted
and achieved huge success recently, most of them are heuristical, and there
lacks a unified theory for algorithm design. In this work, we fill this blank
by developing a mutual information theory for deep fair clustering and
accordingly designing a novel algorithm, dubbed FCMI. In brief, through
maximizing and …

algorithm algorithm design arxiv clustering data design fair gender information novel race rna sequencing success theory work