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|Abstract:||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.|
|Citation:||Jiang, Heinrich, Jennifer Jang, and Samory Kpotufe. "Quickshift++: Provably Good Initializations for Sample-Based Mean Shift." In Proceedings of the 35th International Conference on Machine Learning, PMLR 80, pp. 2294-2303. 2018.|
|Type of Material:||Conference Article|
|Series/Report no.:||Proceedings of Machine Learning Research;|
|Journal/Proceeding Title:||Proceedings of the 35th International Conference on Machine Learning, PMLR|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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