Fair CCA for Fair Representation Learning: An ADNI Study
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911186636767232 |
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| author | Hou, Bojian Wang, Zhanliang Zhou, Zhuoping Tong, Boning Wang, Zexuan Bao, Jingxuan Duong-Tran, Duy Long, Qi Shen, Li |
| author_facet | Hou, Bojian Wang, Zhanliang Zhou, Zhuoping Tong, Boning Wang, Zexuan Bao, Jingxuan Duong-Tran, Duy Long, Qi Shen, Li |
| contents | Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes crucial in machine learning, fair CCA has gained attention. However, previous approaches often overlook the impact on downstream classification tasks, limiting applicability. We propose a novel fair CCA method for fair representation learning, ensuring the projected features are independent of sensitive attributes, thus enhancing fairness without compromising accuracy. We validate our method on synthetic data and real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), demonstrating its ability to maintain high correlation analysis performance while improving fairness in classification tasks. Our work enables fair machine learning in neuroimaging studies where unbiased analysis is essential. Code is available in https://github.com/ZhanliangAaronWang/FR-CCA-ADNI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09382 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fair CCA for Fair Representation Learning: An ADNI Study Hou, Bojian Wang, Zhanliang Zhou, Zhuoping Tong, Boning Wang, Zexuan Bao, Jingxuan Duong-Tran, Duy Long, Qi Shen, Li Machine Learning Artificial Intelligence Computers and Society Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes crucial in machine learning, fair CCA has gained attention. However, previous approaches often overlook the impact on downstream classification tasks, limiting applicability. We propose a novel fair CCA method for fair representation learning, ensuring the projected features are independent of sensitive attributes, thus enhancing fairness without compromising accuracy. We validate our method on synthetic data and real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), demonstrating its ability to maintain high correlation analysis performance while improving fairness in classification tasks. Our work enables fair machine learning in neuroimaging studies where unbiased analysis is essential. Code is available in https://github.com/ZhanliangAaronWang/FR-CCA-ADNI. |
| title | Fair CCA for Fair Representation Learning: An ADNI Study |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2507.09382 |