GPLVM-WPHM

Dimensionality reduction tool for survival (time-to-event) data.
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Updated 7 Jun 2015

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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 .

MATLAB Release Compatibility
Created with R2014b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Version Published Release Notes
1.2.0.0

Discontinued use of Laplace approximation. This will achieve greater computational speed.

Added an example of dimensionality detection.

1.1.0.0

Updated description

1.0.0.0