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Sliced Kernelized Stein Discrepancy
arXiv - CS - Machine Learning Pub Date : 2020-06-30 , DOI: arxiv-2006.16531 Wenbo Gong, Yingzhen Li, Jos\'e Miguel Hern\'andez-Lobato
arXiv - CS - Machine Learning Pub Date : 2020-06-30 , DOI: arxiv-2006.16531 Wenbo Gong, Yingzhen Li, Jos\'e Miguel Hern\'andez-Lobato
Kernelized Stein discrepancy (KSD), though being extensively used in
goodness-of-fit tests and model learning, suffers from the
curse-of-dimensionality. We address this issue by proposing the sliced Stein
discrepancy and its scalable and kernelized variants, which employs
kernel-based test functions defined on the optimal onedimensional projections
instead of the full input in high dimensions. When applied to goodness-of-fit
tests, extensive experiments show the proposed discrepancy significantly
outperforms KSD and various baselines in high dimensions. For model learning,
we show its advantages by training an independent component analysis when
compared with existing Stein discrepancy baselines. We further propose a novel
particle inference method called sliced Stein variational gradient descent
(S-SVGD) which alleviates the mode-collapse issue of SVGD in training
variational autoencoders.
中文翻译:
切片 Kernelized Stein 差异
Kernelized Stein 差异 (KSD) 虽然广泛用于拟合优度测试和模型学习,但受到维数灾难的影响。我们通过提出切片 Stein 差异及其可扩展和内核化的变体来解决这个问题,它采用在最佳一维投影上定义的基于内核的测试函数,而不是高维的完整输入。当应用于拟合优度测试时,大量实验表明,所提出的差异在高维度上明显优于 KSD 和各种基线。对于模型学习,与现有的 Stein 差异基线相比,我们通过训练独立组件分析来展示其优势。
更新日期:2020-07-01
中文翻译:
切片 Kernelized Stein 差异
Kernelized Stein 差异 (KSD) 虽然广泛用于拟合优度测试和模型学习,但受到维数灾难的影响。我们通过提出切片 Stein 差异及其可扩展和内核化的变体来解决这个问题,它采用在最佳一维投影上定义的基于内核的测试函数,而不是高维的完整输入。当应用于拟合优度测试时,大量实验表明,所提出的差异在高维度上明显优于 KSD 和各种基线。对于模型学习,与现有的 Stein 差异基线相比,我们通过训练独立组件分析来展示其优势。