dPCA
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machenslab
An implementation of demixed Principal Component Analysis (a supervised linear dimensionality reduction technique)
Is there a straightforward way to replicate the MATLAB implementation's `'combinedParams'` behavior using the Python `dPCA` code? I would like to do a grouping similar to the stimulus, decision, interaction,...
Hello, I have been applying dPCA in matlab and now I am trying to do the same in Python (in order to learn). When I am trying to do the...
Hi. Just to make sure whether it's normal to be much less than 1 (about 0.5) when having all the explained_variance_ratio_ added up in dPCA (python)? if not, where should...
So I am implementing this on a Neurons * Time point * Category matrix. Below is some description of my inputs: ``` % size(binnedDataStack) = [276 44 8]; margNames =...
My `trialX` is slightly unbalanced, so I followed the instructions `If different combinations of features have different number of trials, then set n_samples to the maximum number of trials and...
Hi, I'm trying to run dPCA on this [dataset](https://gin.g-node.org/sreinert/Category-learning_mPFC). I suppose that my problem stems either from the shape of the input array or from my erroneous understanding of labels....
I'm a little confused about whether the dpca.m used 're-balancing' or not. I have thought that the dpca.m didn't use 're-balancing'(the default setting is to accept 'balanced' data), while the...
Been using dPCA for a little bit and noticed when I compared to PCA(whether doing a supervised group average PCA or a trial average PCA), that the cumulative variance explained...
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An implementation of demixed Principal Component Analysis (a supervised linear dimensionality reduction technique)