We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic information. We propose a conceptually simple approach that allows for an analytic solution similar to standard PCA and can be kernelized. Our methods have the same complexity as standard PCA, or kernel PCA, and run much faster than existing methods for fair PCA based on semidefinite programming or manifold optimization, while achieving similar results.
翻译:我们重温了公平主要成分分析(PCA)的问题。 公平主要成分分析(PCA)的目标是了解混淆人口信息的数据的最佳低端线性近似值。 我们提出了一个概念上简单的方法,允许一种类似于标准五氯苯甲醚的分析性解决方案,并且可以被内分解。 我们的方法与标准五氯苯甲醚(或内核五氯苯甲醚 ) 一样复杂,而且比基于半无限期编程或多重优化的公平五氯苯甲醚现有方法要快得多,同时取得类似结果。</s>