Parametric UMAP is a non-parametric graph-based dimensionality reduction algorithm that extends the second step of UMAP to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding.
Source: Parametric UMAP embeddings for representation and semi-supervised learningPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Dimensionality Reduction | 3 | 50.00% |
Data Visualization | 2 | 33.33% |
Embeddings Evaluation | 1 | 16.67% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |