using simple autoencoder for feature selection
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17-12-2020 - |
Pergunta
I am using a simple autoencoder to extract the informative features and I have multiple Q:
I know that the features extracted will be a linear combination of the original features so I consider that the feature that has a larger mean weight (has the highest percentage in the formation of new features) will be important so I will take that features but I don't know if this is true or not
the second things is that I want to apply the grid search to find the optimal hyperparameters for the model but I can't do that please if anyone can help me in this and save my life
Solução
Autoencoders normally aren't linear models. If you make them linear (i.e. you create a shallow Autoencoder with linear activations) then you get exactly a PCA result. The power of Neural Networks is their non-linearity, if you want to stick with linearity go for PCA imho.
Keep a Train-Validation-Test set split, and try different configurations of hyperparams checking their performance on Validation data. Alternatively there are many libraries, such as
hyperopt
, that let you implement more sophisticated Bayesian hyperparameter searches, but unless you want to be published at a conference or win some competition it's a bit overkill. If you're still interested, the internet is plenty of tutorials like this one.