Search Results

I'm using PCA from sckit-learn and I'm getting some results which I'm trying to interpret, so I ran into question - should I subtract the mean (or perform standardization) before using PCA, or is this … But one things still puts me in doubt - why scale when PCA considers the directions of greatest variance? Aren't I going to break this when standardizing the data before PCA? …

Because of that, I need to do PCA. Some of the features are noisy, others are not. My question is - does it make to do some kind of smoothing (filtering) such as moving average, before doing PCA? …

I have a dataset which contains multivariate time series and I would try applying PCA on it, but I'm not sure how to do it. … Would it make sense to extend my approach and just add samples from other time series in the dataset, and then run PCA on all of that? …

I was planning to use PCA to reduce the dimensionality as to be able to notice such anomalies better. … But then I thought about it and made a counter example how PCA could even make things worse, even though the space has less dimensions. Here's an example. …

To be able to do so I'm performing PCA before clustering, and before that I'm performing standardization of my parameters. … As the normalization is not good, neither is the PCA and clustering of course suffers from parameters on different scale so clusters are hidden due to parameters on higher scale which act as "boosted". …