I’m working on a classification system which consists of an auto-encoder for feature learning and logistic regression for classification. The system has five hyper-parameters as enumerated below.
- Number of features it's learning via auto-encoder
- Weight decaying parameter of the auto-encoder
- Weight decaying parameter of the logistic regression
- Sparsity parameter of the auto-encoder
- The weight of the sparsity penalty term of the auto-encode
I’m planning to use random search for obtaining optimum values for these parameters. More information about the random search for hyper-parameter optimization can be found in this paper
My question is, in order to perform a random search we need to identify the appropriate ranges for each hyper-parameter. For example, the weight decaying parameter of the auto-encoder belongs to
So do you know a published paper to extract these initial values?