Manifold learning from structured kernels and out of sample extensions
Diffusion Maps (DM) and other kernel methods are utilized for the analysis of high dimensional big data. The DM method uses a Markovian diffusion process to model and analyze data. This thesis proposes a combination of techniques aimed to extend kernel methods to reduce their associated computationa...
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| Aineistotyyppi: | Väitöskirja |
| Kieli: | eng |
| Julkaistu: |
2014
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| Aiheet: | |
| Linkit: | https://jyx.jyu.fi/handle/123456789/103733 |