Research map / Learning theory and algorithms / Clustering
UEQMS: UMAP Embedded Quick Mean Shift Algorithm for High Dimensional Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine UMAP dimensionality reduction with QMS | QMS · MNIST | NMI: 0.83 -> 0.89 | Helped |
| Avoid duplicate updates by clustering unique shifted positions with repetition counts | MS++ · Phone Accelerometer | RT (seconds): 1599.34s -> 37.49s | Helped |
| Use QMS instead of MS++ | MS++ · Phone Accelerometer | ARI: 0.0897 -> 0.0897 | No clear effect |
| Use the modified MS++ update to handle non-smooth boundary cases | MS++ · Clustering; theoretical convergence | convergence guarantee | Helped |
| Use UMAP followed by QMS rather than the evaluated automated clustering baselines | High-dimensional real datasets · ALLAML, arcene, GLIOMA, leukemia, orlraws10P, TOX 171, lymphoma, cacmcisi, dermatology, iono, segment, vehicle, vowel, wdbc, and warpAR10P | AMI and ARI | Mixed |
| Use UMAP followed by QMS rather than deep clustering methods | High-dimensional image clustering · MNIST, USPS, Fashion, and pendigit | ACC | Mixed |
Closest papers by meaning
- Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm (AAAI 2021 · similarity 0.88)
- Quickshift++: Provably Good Initializations for Sample-Based Mean Shift (ICML 2018 · similarity 0.87)
- Simple, Scalable and Effective Clustering via One-Dimensional Projections (NeurIPS 2023 · similarity 0.85)
- On Convergence of Epanechnikov Mean Shift (AAAI 2018 · similarity 0.85)
- MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking (CVPR 2021 · similarity 0.84)
- Exploring a Principled Framework for Deep Subspace Clustering (ICLR 2025 · similarity 0.82)
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