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Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by Coifman and Lafon which computes a family of embeddings of a data set into Euclidean space (often low-dimensional) whose coordinates can be computed from the eigenvectors and eigenvalues of a diffusion operator on the data. The Euclidean distance between points in…
Applications, Application & Definition of diffusion maps
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diffusion displaystyle data maps points matrix kernel manifold distance graph laplacian alpha set one step connection eigenvectors map space also
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| principal component analysis | instance of | Different from linear dimensionality reduction methods | 0.80 | text |
| principal component analysis | instance of | This is a major difference with methods | 0.80 | text |
| where correlations between all data points are taken into account at once.Given | instance of | This is a major difference with methods | 0.80 | text |
| Diffusion map | related to Algorithm | The | 0.60 | section |
| Diffusion map | related to Algorithm | Step | 0.60 | section |
| Diffusion map | related to Algorithm | Given | 0.60 | section |
| Diffusion map | related to Application | In | 0.60 | section |
| Diffusion map | related to Application | Nadler | 0.60 | section |
| Diffusion map | related to Application | Fokker | 0.60 | section |
| Diffusion map | related to Application | Planck | 0.60 | section |
| Diffusion map | related to Application | They | 0.60 | section |
| Diffusion map | related to Application | Laplace | 0.60 | section |
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