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My first post....

Im doing a project that extracts certain features from music files. These "feautures" will/may become the inputs to a neural network. I have 12 features in total which will correspond to a maximum of 12 inputs to the neural network.

Essentially I will have 12 columns of data, 1 column of data for each feature. eg 10 music files will produce 10 rows of data for each feature/column. eg Amplitude could be column 1.

Anyway, here comes my maths question. I am not an expert at Maths as Ive only done basic math at university but Im willing to learn and am a fast learner.

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I want to decide which input features/columns of data are the most important and any relationshipd between them etc. Maybe some sort of classification also but Im not sure?

I have been told that PCA or Principle Components Analysis could be the best way of doing this. I don't have any knowledge of this but a search in Google tells me that this is working out SD and other parameters.

Also, I have been told that classifiers such as Bayesian classifiers could be worth a look.

Im just looking for advice for good maths experts on here. How would you tackle the problem, what techniques would you use? Is it important to look at the relationships between the input data sets?

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# Determining the Importance of Certain Data Types (PCA?)

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