On the Consistency of Quick Shift
Authors: Heinrich Jiang
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that Quick Shift recovers the modes of an arbitrary multimodal density at a minimax optimal rate under mild nonparametric assumptions. This provides an alternative to known procedures with similar statistical guarantees; however such procedures only recover the modes but fail to inform us how to assign the sample points to a mode which is critical for clustering. Quick Shift on the other hand recovers both the modes and the clustering assignments with statistical consistency guarantees. Moreover, Quick Shift s ability to do all of this has been extensively validated in practice. |
| Researcher Affiliation | Industry | Heinrich Jiang Google Inc. 1600 Amphitheatre Parkway, Mountain View, CA 94043 heinrich.jiang@gmail.com |
| Pseudocode | Yes | Algorithm 1 Quick Shift; Algorithm 2 Quick Shift Cluster Tree Estimator; Algorithm 3 Quick Shift Modal Regression |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the release of source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical consistency guarantees and does not report on experiments using a specific, publicly available dataset that requires access information. |
| Dataset Splits | No | The paper focuses on theoretical consistency guarantees and does not describe experimental data splits (train, validation, test) for reproducibility. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not report on empirical experiments that would require detailed experimental setup parameters like hyperparameters or training configurations. |