Research map / Learning theory and algorithms / Clustering
Sparse Embedded $k$-Means Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace dense random sign projection (RP) with sparse embedding | k-means dimensionality reduction · COIL20, SECTOR, RCV1, ILSVRC2012 | clustering accuracy | Helped |
| replace dense random sign projection (RP) with sparse embedding | k-means dimensionality reduction · COIL20, SECTOR, RCV1, ILSVRC2012 | dimension reduction time | Helped |
| increase number of embedded dimensions | all dimension reduction methods · COIL20, SECTOR, RCV1, ILSVRC2012 | clustering accuracy | Helped |
| apply dimensionality reduction before k-means instead of full dimensions | k-means · COIL20, SECTOR, RCV1, ILSVRC2012 | clustering time | Helped |
Closest papers by meaning
- Simple, Scalable and Effective Clustering via One-Dimensional Projections (NeurIPS 2023 · similarity 0.92)
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- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.87)
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