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Maximum Margin Dirichlet Process Mixtures for Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · Frame | F-score: 0.43 -> 0.60 | Helped |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · Jain | F-score: 0.59 -> 0.73 | Helped |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · Wdbc | F-score: 0.43 -> 0.85 | Helped |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · Wine | F-score: 0.36 -> 0.68 | Helped |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · Aggregation | F-score: 0.91 -> 0.79 | Hurt |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · MNIST | F-score: 0.18 -> 0.368 | Helped |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · Glass | V-score: 0.43 -> 0.38 | Hurt |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · Reuters21578 | V-score: 0.472 -> 0.335 | Hurt |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · Reuters21578 | adjusted Rand Index: 0.383 -> 0.416 | Helped |
| learn component parameters discriminatively with online max-margin (replace generative Gaussian DPM) | Dirichlet process mixture clustering · 20 Newsgroups subset | F-score: 0.094 -> 0.10 | No clear effect |
Closest papers by meaning
- A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering (NeurIPS 2017 · similarity 0.89)
- X-DMM: Fast and Scalable Model Based Text Clustering (AAAI 2019 · similarity 0.87)
- Global-Local Dirichlet Processes for Clustering Grouped Data in the Presence of Group-Specific Idiosyncratic Variables (ICML 2025 · similarity 0.87)
- On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances (ICML 2023 · similarity 0.86)
- Learning Mixtures of Gaussian Processes through Random Projection (ICML 2024 · similarity 0.85)
- State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives (NeurIPS 2026 · similarity 0.83)
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