GPLVM-WPHM
This is a combination of the non-linear dimensionality reduction Gaussian process latent variable model (GPLVM) and the Weibull proportional hazard model (WPHM). It is suitable for high dimensional data with time-to-event measurements. That is, survival analysis with high dimensional covariates. This work is based on the publication: http://arxiv.org/abs/1406.0812. Please don't hesitate to contact me if there are any issues.
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James Barrett (2024). GPLVM-WPHM (https://www.mathworks.com/matlabcentral/fileexchange/48565-gplvm-wphm), MATLAB Central File Exchange. Extrait(e) le .
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v_1_2_0/
v_1_2_0/example/
v_1_2_0/wphm/
Version | Publié le | Notes de version | |
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1.2.0.0 | Discontinued use of Laplace approximation. This will achieve greater computational speed. Added an example of dimensionality detection. |
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1.1.0.0 | Updated description |
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1.0.0.0 |
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