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A tractable latent variable model for nonlinear dimensionality reduction
We propose a latent variable model to discover faithful low-dimensional representations of high-dimensional data. The model computes a low-dimensional embedding that aims to preserve neighborhood relationships encoded by a sparse graph. The model both leverages and extends current leading approaches...
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| I publikationen: | Proc Natl Acad Sci U S A |
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| Huvudupphovsman: | |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
National Academy of Sciences
2020
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7354940/ https://ncbi.nlm.nih.gov/pubmed/32571935 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1916012117 |
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