Quickshift++: Provably Good Initializations for Sample-Based Mean Shift

We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this m...

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Bibliographic Details
Main Authors Jiang, Heinrich, Jang, Jennifer, Kpotufe, Samory
Format Journal Article
LanguageEnglish
Published 21.05.2018
Subjects
Computer Science - Learning
Statistics - Machine Learning
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Summary:We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then show strong clustering performance on real datasets as well as promising applications to image segmentation.
DOI:10.48550/arxiv.1805.07909