On the Consistency of Quick Shift
Heinrich Jiang
TL;DR
This paper provides finite sample guarantees for Quick Shift's ability to recover modes and clusters, and applies these results to develop a consistent modal regression method.
Contribution
It offers the first finite sample consistency guarantees for Quick Shift and extends these results to modal regression.
Findings
Quick Shift is statistically consistent for mode and cluster recovery.
The paper introduces a new consistent modal regression algorithm.
Theoretical guarantees hold under mild distributional assumptions.
Abstract
Quick Shift is a popular mode-seeking and clustering algorithm. We present finite sample statistical consistency guarantees for Quick Shift on mode and cluster recovery under mild distributional assumptions. We then apply our results to construct a consistent modal regression algorithm.
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Taxonomy
TopicsWeb Data Mining and Analysis · Algorithms and Data Compression · Constraint Satisfaction and Optimization