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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.
Cite As
James Barrett (2026). GPLVM-WPHM (https://www.mathworks.com/matlabcentral/fileexchange/48565-gplvm-wphm), MATLAB Central File Exchange. Retrieved .
General Information
- Version 1.2.0.0 (378 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 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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