Efficient MATLAB implementation of online Principal Subspace Projection algorithms (Fast Similarity Matching, Incremental PCA[2,3], and Candid Covariance Incremental PCA[2,4])
For the more complete Python version please go to the link online-psp
Clone the repository or unzip the source and add recursively folders from the src folder to the MATLAB path
k -> subspace dimension d -> number of features % we suggest to standardize data using the standardize_data function [X,~,~] = standardize_data(X,0,0); fsm = FSM(k, d, , , , ); for i = 1:n fsm.fit_next(x(:,i)'); end components = fsm.get_components();
For more detailed examples explore the demo_XXX.m files
 Pehlevan, Cengiz, Anirvan M. Sengupta, and Dmitri B. Chklovskii. "Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?." Neural computation 30, no. 1 (2018): 84-124.
 Cardot, Hervé, and David Degras. "Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?." arXiv preprint arXiv:1511.03688 (2015).
 Arora, R., Cotter, A., Livescu, K. and Srebro, N., 2012, October. Stochastic optimization for PCA and PLS. In Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on (pp. 861-868). IEEE.
 Weng, J., Zhang, Y. and Hwang, W.S., 2003. Candid covariance-free incremental principal component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8), pp.1034-1040.
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