Compressive Gaussian Mixture estimation

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

Volumn , Issue , 2013, Pages 6024-6028

  Bourrier, Anthony ^a   Gribonval, Remi ^a   Perez, Patrick ^b  

^a TECHNICOLOR   (France)

^b INRIA   (France)

Author keywords

compressive learning; compressive sensing; database sketch; Gaussian mixture estimation

Indexed keywords

COMPRESSIVE LEARNING; COMPRESSIVE SENSING; COMPUTATIONAL TIME; GAUSSIAN MIXTURES; MIXTURE ESTIMATION; PRIVACY PRESERVING; PROBABILITY MODELING; RECONSTRUCTION ALGORITHMS;

PROBABILITY DISTRIBUTIONS; SIGNAL RECONSTRUCTION;

ALGORITHMS;

EID: 84890490551     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6638821     Document Type: Conference Paper

References (12)

1. T. Blumensath and M. Davies, "Iterative hard thresholding for compressed sensing," Applied and Computational Harmonic Analysis, vol. 27, no. 3, pp. 265-274, 2009.
2. A. C. Gilbert, Y. Kotidis, S. Muthukrishnan, and M. J. Strauss, "How to summarize the universe: Dynamic maintenance of quantiles," in Conference on Very Large Databases, 2002.
3. G. Cormode and S. Muthukrishnan, "An improved data stream summary: The count-min sketch and its applications," Journal of Algorithms, vol. 55, no. 1, pp. 58-75, 2005.
4. G. Cormode and M. Hadjieleftheriou, "Methods for finding frequent items in data streams," VLDB Journal, vol. 19, no. 1, pp. 3-20, 2010.
5. N. Thaper, S. Guha, P. Indyk, and N. Koudas, "Dynamic multidimensional histograms," in ACM SIGMOD International conference on Management of data, 2002.
6. F. Bunea, A. Tsybakov, M. Wegkamp, and A. Barbu, "Spades and mixture models," Annals of Statistics, vol. 38, no. 4, pp. 2525-2558, 2010.
7. K. Bertin, E. Le Pennec, and V. Rivoirard, "Adaptive Dantzig density estimation," Annales de lInstitut Henri Poincare (B) Probabilites et Statistiques, vol. 47, no. 1, pp. 43-74, 2011.
8. R. Calderbank, R. Schapire, and S. Jafarpour, "Compressed learning : Universal sparse dimensionality reduction and learning in the measurement domain," Preprint, 2009.
9. O.-A. Maillard and R. Munos, "Compressed least-squares regression," in Neural Information Processing Systems Conference, 2009.
10. X. Pitkow, "Compressive neural representation of sparse, highdimensional probabilities," in Neural Information Processing Systems Conference, 2012.
11. P. Liang and D. Klein, "Online EM for unsupervised models," in Conference of the North American Chapter of the Association for Computational Linguistics, 2009.
12. L. Bottou and O. Bousquet, "The tradeoffs of large scale learning," in Neural Information Processing Systems Conference, 2008.