Learning Fair Canonical Polyadical Decompositions using a Kernel Independence Criterion

2021/04/27 by Kevin Kim, Kim, Kevin, Alex Gittens +1 · 1 citation
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Tensor decomposition and applications

paper · pdf · doi:10.48550/arxiv.2104.13504

openalex publication_date 2021/04/27 · openalex created_date 2021/05/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Analysis of Individual Differences in Multidimensional Scaling Via an N-way Generalization of “Eckart-Young” Decomposition
• Tensor Decomposition for Signal Processing and Machine Learning

Cited by

• Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation

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