Bilateral random projections

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2012 IEEE International Symposium on Information Theory Proceedings (ISIT), 2012, pp. 1286 - 1290
Issue Date:
2012-01
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Low-rank structure have been profoundly studied in data mining and machine learning. In this paper, we show a dense matrix X's low-rank approximation can be rapidly built from its left and right random projections Y1 = XA1 and Y2 = XT A2, or bilateral random projection (BRP). We then show power scheme can further improve the precision. The deterministic, average and deviation bounds of the proposed method and its power scheme modification are proved theoretically. The effectiveness and the efficiency of BRP based low-rank approximation is empirically verified on both artificial and real datasets.
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