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[FIX] Normalize data to zero mean and unit variance before dimension estimation #636

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merged 11 commits into from
Feb 5, 2021
Merged

[FIX] Normalize data to zero mean and unit variance before dimension estimation #636

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65917dd
Normalize data to zero mean and unit variance in ma_pca
notZaki Dec 28, 2020
d9c3dc2
Use normalized data as input to ma_pca
notZaki Jan 15, 2021
c43860d
Revert first change: do not re-scale input in ma_pca
notZaki Jan 15, 2021
34c2836
Use consistent normalization strategy in ma_pca
notZaki Jan 16, 2021
c7e475e
Remove outdated comments
notZaki Jan 16, 2021
68e455e
Merge branch 'master' into normalizeBeforeMAPCA
notZaki Jan 16, 2021
9b72780
Update regression test data; normalization produces fewer figures
notZaki Jan 16, 2021
c60d57e
Merge branch 'master' into normalizeBeforeMAPCA
notZaki Jan 25, 2021
1d89983
Merge branch 'master' into normalizeBeforeMAPCA
notZaki Jan 30, 2021
9f3f952
Normalize along spatial dimension in mapca; Matches GIFT
notZaki Jan 30, 2021
2b385db
Update test output; updated mapca finds 3 more components
notZaki Jan 30, 2021
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2 changes: 0 additions & 2 deletions tedana/decomposition/ma_pca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -478,9 +478,7 @@ def ma_pca(data_nib, mask_nib, criteria='mdl'):
maskvec = np.reshape(mask_nib, Nx * Ny * Nz, order='F')
data_non_normalized = data_nib_V[maskvec == 1, :]
scaler = StandardScaler(with_mean=True, with_std=True)
# TODO: determine if tedana is already normalizing before this
data = scaler.fit_transform(data_non_normalized) # This was X_sc
data = data_non_normalized

LGR.info('Performing SVD on original OC data...')
V, EigenValues = _icatb_svd(data, Nt)
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