This work proposes to learn fair low-rank tensor decompositions by regularizing the Canonical Polyadic Decomposition factorization with the kernel Hilbert-Schmidt independence criterion (KHSIC). It is shown, theoretically and empirically, that a small KHSIC between a latent factor and the sensitive features guarantees approximate statistical parity. The proposed algorithm surpasses the state-of-the-art algorithm, FATR (Zhu et al., 2018), in controlling the trade-off between fairness and residual fit on synthetic and real data sets.

翻译：这项工作建议,通过将Canonical Polical Policadic 分解因数与Hilbert-Schmidt内部独立标准(KHSIC)的正规化,学习公平、低级的分解,从理论上和从经验上表明,在潜在因素和敏感特征之间的小型KHSIC保障了统计均等的近似值,拟议的算法超过了控制公平与合成和真实数据集的剩余相适应性之间的权衡的先进算法,即FATR(Zhu等人,2018年)。