Estimating the largest eigenvalues by the power and Lanczos algorithms with a random start
SIAM Journal on Matrix Analysis and Applications
Matrix computations (3rd ed.)
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Randomized Algorithm for Principal Component Analysis
SIAM Journal on Matrix Analysis and Applications
Computing Steerable Principal Components of a Large Set of Images and Their Rotations
IEEE Transactions on Image Processing
Tapkee: an efficient dimension reduction library
The Journal of Machine Learning Research
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Recently popularized randomized methods for principal component analysis (PCA) efficiently and reliably produce nearly optimal accuracy—even on parallel processors—unlike the classical (deterministic) alternatives. We adapt one of these randomized methods for use with data sets that are too large to be stored in random-access memory (RAM). (The traditional terminology is that our procedure works efficiently out-of-core.) We illustrate the performance of the algorithm via several numerical examples. For example, we report on the PCA of a data set stored on disk that is so large that less than a hundredth of it can fit in our computer's RAM.