id,summary,reporter,owner,description,type,status,priority,milestone,component,version,resolution,keywords,cc
2497,Improve scaling of inputs and optionally target values for the Gaussian process,gkronber,gkronber,"There are some issues related to scaling of data in the Gaussian process model.
1. input features are always scaled to the range [0..1]. This has the effect that the period length in the periodic covariance function cannot be set to a natural value. Assume that we have a time series with daily observations where we want to model a weekly periodic signal. In this case it would be great if we could set the period = 7 in the periodic covariance function. However, because of scaling the correct value depends on minimum and maximum observed day.
2. All hyper-parameters are initialized randomly using a log-uniform distribution. This is a good assumption for length scales if all input variables have been scaled to the same range. But for other hyper-parameters (esp. for hyper-parameters for mean functions) this initialization might not be ideal. Especially, for the constant mean function and for the noise hyper-parameter of the Gaussian likelihood it might be better to initialize these hyperparameters to the empirical mean and variance.",defect,closed,medium,HeuristicLab 3.3.13,Algorithms.DataAnalysis,3.3.12,done,,