https://github.com/atarashansky/pcasparse
A function that can run PCA on sparse inputs using LinearOperators to perform implicit mean centering prior to SVD.
Science Score: 36.0%
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Repository
A function that can run PCA on sparse inputs using LinearOperators to perform implicit mean centering prior to SVD.
Basic Info
- Host: GitHub
- Owner: atarashansky
- Language: Python
- Default Branch: master
- Size: 3.91 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
PCAsparse
A function that can run PCA on scipy sparse inputs using LinearOperators to perform implicit mean centering prior to SVD.
This function was written thanks to helpful discussions found in this scikit-learn issue: https://github.com/scikit-learn/scikit-learn/issues/12794
This function is a little slower than sklearn.decomposition.TruncatedSVD as TruncatedSVD is run using the randomized SVD solver, which does not accept as input a scipy.sparse.linalg.LinearOperator. scipy.sparse.linalg.svds does accept a LinearOperator as input but does not have randomized SVD as an available solver (only lobpcg and arpack).
Usage
``` from sparsepca import sparsepca
components_ -- the loadings
PCs -- the principal components
X -- a scipy.sparse matrix
PCs,components_ = sparse_pca(X,100) #runs PCA on X for 100 principal components ```
Owner
- Login: atarashansky
- Kind: user
- Repositories: 3
- Profile: https://github.com/atarashansky
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| Name | Commits | |
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