PCA(Principal Component Analysis), SVD(Singular Value Decomposition), LSA(Latent Semantic Analysis)
tags: reduced SVD, Out-of-Core SVD, Stochastic SVD,
https://github.com/jhlch/svd-benchmark
redsvd
http://nuit-blanche.blogspot.ru/2011/12/redsvd-randomized-singular-value.html
https://code.google.com/p/redsvd/
http://cims.nyu.edu/~tygert/software.html
http://tygert.com/software.html
truncated SVD (LSA/LSI)
http://radimrehurek.com/gensim/models/lsimodel.html
Python:
TruncatedSVD
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html
RandomizedPCA
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.RandomizedPCA.html#sklearn.decomposition.RandomizedPCA
https://github.com/ktaneishi/pyredsvd
Gensim
https://radimrehurek.com/gensim/models/lsimodel.html
Fast Randomized SVD
https://github.com/facebook/fbpca
https://research.facebook.com/blog/294071574113354/fast-randomized-svd/
PCA comparision
https://github.com/vighneshbirodkar/pca
R-PCA
https://github.com/dganguli/robust-pca
PROPACK
http://sun.stanford.edu/~rmunk/PROPACK/
Out-of-Core SVD
https://sites.google.com/site/yoelshkolnisky/software
https://github.com/sergeyvoronin/LowRankSVDCodes
C++
https://github.com/gabraham/flashpca
CUDA
https://github.com/sergeyvoronin/LowRankMatrixDecompositionCodes
Cluster version:
https://github.com/SiddharthMalhotra/sPCA
Papers:
"Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions"
http://arxiv.org/abs/0909.4061
Presentations:
"Randomized methods for computing the Singular Value Decomposition (SVD) of very large matrices"
http://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf
"Randomized Algorithms for Very Large-Scale Linear Algebra"
https://www.cse.cuhk.edu.hk/irwin.king/_media/presentations/randomized_algorithm_for_very-large_scale_linear_algebra.pdf
Other:
LDA
https://github.com/Microsoft/lightlda
Blogs:
https://www.reddit.com/r/MachineLearning/comments/2zresl/how_to_pca_large_data_sets_im_running_out_of/