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I am very new to ML and have limited knowledge about it. I am having issue in feature normalization process. I have understood from the post that we need to normalize the training features and scale the test/validation features with the training data. I am facing issue in the implementation as in my case my training samples have fixed dimension but the dimension of validation and test data is variable. So, I can apply zero mean unit variance for training data but I am not sure how can I normalize the validation/test data samples as the sample dimension/length is variable/not fixed.

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  • $\begingroup$ Can you explain why your validation samples have a different dimension in comparison to training data? The basis for many ML algos to work is that the train , validation and test data belong to the same underlying distribution $\endgroup$ – Jayaram Iyer Apr 28 at 4:19
  • $\begingroup$ Can you explain why training and test data are different? In my understanding, this can bring some issues, as your system has been trained with a different distribution of data. $\endgroup$ – Raul Alvarez Apr 28 at 6:51
  • $\begingroup$ @RaulAlvarez The paper I am trying to implement says that their model uses the fixed sizes (512, 128) samples during training and complete audio clip as one sample during testing and validation. $\endgroup$ – skiii gairola May 2 at 11:53
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That is a common case on image and audio processing, you need to find a way in which dimensions stay the same, such as normalizing per channel.

If you have a 1D vector of features, taking mean and variance of all variables will end up normalizing it in a way, it works in Computer Vision like a charm. It is also a way to reduce the space cost of your normalizing algorithm.

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  • $\begingroup$ In my case, I am dealing with mono-channel audio. and frequency bin is fixed to 128 but time-frames are different in each audio clip. I tried to normalize across frequency bin i.e., calculated mean as a vector of length 128 (mean for each frequency bin) but it is not working. $\endgroup$ – skiii gairola May 2 at 10:53

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