GPLVM-WPHM

Dimensionality reduction tool for survival (time-to-event) data.
417 Downloads
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 (2024). 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

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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