zifn / CS289_Coltrims_ML

repo for the final project of berkeley CS289
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Perform PCA after featurization to identify most important dimensions #26

Closed sajantanand closed 3 years ago

sajantanand commented 3 years ago

Use sklearn PCA to reduce dimensions of data matrix after featurization. Will allow for faster clustering. Adjust whitening and PCA functions into a single function that takes number of components and a boolean for whitening as parameters. Basically make this a wrapper of the sklearn function that does the data transforming all in one step.