Mathematics – Numerical Analysis
Scientific paper
2009-08-02
Mathematics
Numerical Analysis
Complete proofs are included on averaging performance
Scientific paper
We propose a second-order accurate method to estimate the eigenvectors of extremely large matrices thereby addressing a problem of relevance to statisticians working in the analysis of very large datasets. More specifically, we show that averaging eigenvectors of randomly subsampled matrices efficiently approximates the true eigenvectors of the original matrix under certain conditions on the incoherence of the spectral decomposition. This incoherence assumption is typically milder than those made in matrix completion and allows eigenvectors to be sparse. We discuss applications to spectral methods in dimensionality reduction and information retrieval.
d'Aspremont Alexandre
Karoui Noureddine El
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