Normalizing out the 1st and more components from the data. This is usefull if the data is seperated in its first component(s) by unwanted or biased variance. Such as sex or experiment location etc.
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Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...
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