Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis

• URL: http://arxiv.org/abs/2105.12778v1
• Date: Wed, 26 May 2021 18:22:33 GMT
• Title: Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis
• Authors: George Wynne and Stanislav Nagy
• Abstract summary: Common $h$-depth and related statistical depths for functional data can be viewed as a kernel mean embedding. This article highlights how the common $h$-depth and related statistical depths for functional data can be viewed as a kernel mean embedding.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Statistical depth is the act of gauging how representative a point is compared to a reference probability measure. The depth allows introducing rankings and orderings to data living in multivariate, or function spaces. Though widely applied and with much experimental success, little theoretical progress has been made in analysing functional depths. This article highlights how the common $h$-depth and related statistical depths for functional data can be viewed as a kernel mean embedding, a technique used widely in statistical machine learning. This connection facilitates answers to open questions regarding statistical properties of functional depths, as well as it provides a link between the depth and empirical characteristic function based procedures for functional data.

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