Fast and efficient dimensionality reduction using structurally random matrices

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2009, Pages 1821-1824

  Do, Thong T ^a   Gan, Lu ^b   Chen, Yi ^a   Nguyen, Nam ^a   Tran, Trac D ^a  

^a JOHNS HOPKINS UNIVERSITY   (United States)

^b BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Compressed sensing; Dimensionality reduction; Johnson Lindenstrauss; Low distortion embedding; Machine learning

Indexed keywords

COMPRESSED SENSING; DIMENSIONALITY REDUCTION; JOHNSON-LINDENSTRAUSS; LOW-DISTORTION EMBEDDING; MACHINE LEARNING;

ACOUSTICS; COMPUTATIONAL COMPLEXITY; RELUCTANCE MOTORS; ROBOT LEARNING; SIGNAL PROCESSING;

SIGNAL RECONSTRUCTION;

EID: 70349211724     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2009.4959960     Document Type: Conference Paper

References (12)

1. T. T. Do, T. D. Tran, and L. Gan, "Fast compressive sampling with structurally random matrices," Proceedings of Acoustics, Speech and Signal Processing, 2008. ICASSP 2008, pp. 3369-3372, May 2008.
2. R. Baraniuk, M. Davenport, R. DeVore, and M.Wakin, "A simple proof of the restricted isometry property for random matrices," To appear in Constructive Approximation.
3. E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. on Information Theory, vol. 52, pp. 489 - 509, Feb. 2006.
4. W. B Johnson and J. Lindenstrauss, "Extensions of Lipschitz mappings into a Hilbert space," Conf. in Modern Analysis and Probability, pp. 189-206, 1984.
5. D. Achlioptas, "Database-friendly random projection: Johnson-Lindenstrauss with binary coins," Journal of Computer and System Sciences, vol. 66, pp. 671-687, 2003.
6. N. Ailon and B. Chazelle, "Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform," Proceedings of the thirty-eighth annual ACM symposium on Theory of computing, vol. 66, pp. 557 - 563, 2006.
7. J. Matousek, "On the variants of the Johnson-Lindenstrauss lemma," Random Structure and Algorithms, vol. 33, pp. 142-156, 2008.
8. N. Ailon and E. Liberty, "Fast dimension reduction using rademacher series on dual BCH codes," To appear in Discrete and Computational Geometry, 2008.
9. E. Liberty, N. Ailon, and A. Singer, "Dense fast random projections and Lean Walsh transform," In RANDOM, Boston, MA, Aug. 2008.
10. G. Lugosi, "Concentration-of-measure inequalities," Lecture notes, Feb. 2006.
11. E. Bingham and H. Mannila, "Random projection in dimensionality reduction: applications to image and text data,," In Proceedings of the seventh ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining, 2001.
12. N. Nguyen, T. T. Do, and T. D. Tran, "A fast and efficient algorithm of low-rank approximation of a matrix,," submitted to Symposium on Theory of Computing, 2009.