用偏最小二乘法构建公平表示，提升预测效率与公平性
PLS-based approach for fair representation learning
- 基于偏最小二乘法构造兼顾预测与公平的特征表示
- 在多个数据集上优于传统公平PCA方法
- 适用于线性和非线性场景，支持核嵌入
我们重新审视公平表示学习问题，提出公平偏最小二乘（Fair PLS）成分。PLS广泛用于统计学中通过生成面向预测的紧凑表示来高效降维。本文提出一种新方法，在构建PLS成分时引入公平性约束。该算法在线性和非线性情形下均有效，可通过核嵌入实现。在多个数据集上的实验评估表明，该方法在性能上优于标准公平PCA方法。
原文摘要 · Abstract (English)
We revisit the problem of fair representation learning by proposing Fair Partial Least Squares (PLS) components. PLS is widely used in statistics to efficiently reduce the dimension of the data by providing representation tailored for the prediction. We propose a novel method to incorporate fairness constraints in the construction of PLS components. This new algorithm provides a feasible way to construct such features both in the linear and the non linear case using kernel embeddings. The efficiency of our method is evaluated on different datasets, and we prove its superiority with respect to standard fair PCA method.
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