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Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Machine Learning · Submitted 26 Sep 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim To verify the effectiveness of the proposed FCMI, we conduct experiments on six benchmarks including a single-cell RNA-seq atlas compared with 11 state-of-the-art methods in terms of five metrics.
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The abstract, sentence by sentence
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 minimizing mutual information, FCMI is designed to achieve four characteristics highly expected by deep fair clustering, \textit{i.e.}, compact, balanced, and fair clusters, as well as informative features. Besides the contributions to theory and algorithm, another contribution of this work is proposing a novel fair clustering metric built upon information theory as well. Unlike existing evaluation metrics, our metric measures the clustering quality and fairness as a whole instead of separate manner. To verify the effectiveness of the proposed FCMI, we conduct experiments on six benchmarks including a single-cell RNA-seq atlas compared with 11 state-of-the-art methods in terms of five metrics. The code could be accessed from \url{ https://pengxi.me}.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.05 | 0.04 | asserted 0.99 |
| s1 | 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. | gap 0.98 | 0.06 | 0.03 | asserted 1.00 |
| s2 | 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. | contribution 1.00 | 0.96 | 0.15 | asserted 1.00 |
| s3 | In brief, through maximizing and minimizing mutual information, FCMI is designed to achieve four characteristics highly expected by deep fair clustering, \textit{i.e.}, compact, balanced, and fair clusters, as well as informative features. | method 0.86 | 0.92 | 0.23 | asserted 1.00 |
| s4 | Besides the contributions to theory and algorithm, another contribution of this work is proposing a novel fair clustering metric built upon information theory as well. | contribution 1.00 | 0.95 | 0.08 | asserted 1.00 |
| s5 | Unlike existing evaluation metrics, our metric measures the clustering quality and fairness as a whole instead of separate manner. | contribution 0.97 | 0.81 | 0.06 | asserted 1.00 |
| s6 | To verify the effectiveness of the proposed FCMI, we conduct experiments on six benchmarks including a single-cell RNA-seq atlas compared with 11 state-of-the-art methods in terms of five metrics. | method 0.80 | 0.97 | 0.16 | asserted 0.99 |
| s7 | The code could be accessed from \url{ https://pengxi.me}. | contribution 0.65 | 0.40 | 0.02 | hedged possibility 0.94 |
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The paper
Original abstract
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 minimizing mutual information, FCMI is designed to achieve four characteristics highly expected by deep fair clustering, \textit{i.e.}, compact, balanced, and fair clusters, as well as informative features. Besides the contributions to theory and algorithm, another contribution of this work is proposing a novel fair clustering metric built upon information theory as well. Unlike existing evaluation metrics, our metric measures the clustering quality and fairness as a whole instead of separate manner. To verify the effectiveness of the proposed FCMI, we conduct experiments on six benchmarks including a single-cell RNA-seq atlas compared with 11 state-of-the-art methods in terms of five metrics. The code could be accessed from \url{ https://pengxi.me}.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Cites it as background 1 paper
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Another approach is to focus on algorithmic designs such as Fairness-Adversarial Loss [ref] , maximizing the conditional mutual information (CMI) between inputs and cluster assignment [ref] given sensitive attributes, Domain Adaptation [ref] , or deep information maximization adaptation network by transferring recognition knowledge from one demographic group to other groups [ref] .
Fairness in Visual Clustering: A Novel Transformer Clustering Approach · Introduction · 0.82
What it cites · 5 sentences without a confident label
Uses the method or tool of 1 paper
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To help understand the working mechanism of FCMI, we first visualize the hidden representation of MNIST-USPS by performing UMAP [ref] on the learned features across the training process.
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction · Visualization · 1.00
Compares with 2 papers
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For the deep fair clustering methods, we could only investigate the performance of DFC [ref] since the code of DFDC [ref] and Towards [ref] are unavailable.
Towards Fair Deep Clustering With Multi-State Protected Variables · Experimental Setups · 0.80
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For the deep fair clustering methods, we could only investigate the performance of DFC [ref] since the code of DFDC [ref] and Towards [ref] are unavailable.
Deep Fair Discriminative Clustering · Experimental Setups · 0.80
Cites as background 2 papers
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Very recently, Zhang et al. [ref] generate fair pseudo cluster assignments to guide the model optimization.
Deep Fair Discriminative Clustering · Related Work · 0.93
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To achieve fair clustering, many studies have been conducted to explore how to incorporate fairness into clustering [ref] .
Towards Fair Deep Clustering With Multi-State Protected Variables · Introduction · 0.94
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