Coding for random projections

31st International Conference on Machine Learning, ICML 2014

Volumn 3, Issue , 2014, Pages 2137-2145

  Li, Ping ^a   Mitzenmacher, Michael ^b   Shrivastava, Anshumali ^c  

^a RUTGERS UNIVERSITY   (United States)

^b HARVARD UNIVERSITY   (United States)

^c Dept of Computer Science Computing and Information Science Cornell University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALUMINUM; ARTIFICIAL INTELLIGENCE; DIGITAL STORAGE; EXPERIMENTS; SUPPORT VECTOR MACHINES;

ADDITIONAL EXPERIMENTS; COMPUTATIONAL SPEED; LARGE-SCALE APPLICATIONS; LINEAR SUPPORT VECTOR MACHINES; NEAR NEIGHBOR SEARCHES; SIMILARITY ESTIMATION; STATISTICAL LEARNING; UNIFORM QUANTIZATION;

CODES (SYMBOLS);

EID: 84919882406     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. Bingham, Ella and Mannila, Heikki. Random projection in dimensionality reduction: Applications to image and text data. In KDD, pp. 245-250, San Francisco, CA, 2001.
2. Bottou, Leon, http://leon.bottou.org/projects/sgd.
3. Buhler, Jeremy and Tompa, Martin. Finding motifs using random projections. Journal of Computational Biology, 9(2):225-242, 2002.
4. Carter, J. Lawrence and Wegman, Mark N. Universal classes of hash functions. In STOC, pp. 106-112, 1977.
5. Charikar, Moses, Chen, Kevin, and Farach-Colton, Martin. Finding frequent items in data streams. Theor. Comput. Sci., 312(1):3-15, 2004.
6. Charikar, Moses S. Similarity estimation techniques from rounding algorithms. In STOC, pp. 380-388, Montreal, Quebec, Canada, 2002.
7. Cormode, Graham and Muthukrishnan, S. An improved data stream summary: The count-min sketch and its applications. Journal of Algorithm, 55(1):58-75, 2005.
8. Dasgupta, Sanjoy. Learning mixtures of gaussians. In FOCS, pp. 634-644, New York, 1999.
9. Dasgupta, Sanjoy. Experiments with random projection. In UAI, pp. 143-151, Stanford, CA, 2000.
10. Datar, Mayur, Immorlica, Nicole, Indyk, Piotr, and Mirrokn, Vahab S. Locality-sensitive hashing scheme based on p-stable distributions. In SCG, pp. 253 - 262, Brooklyn, NY, 2004.
11. Fan, Rong-En, Chang, Kai-Wei, Hsieh, Cho-Jui, Wang, Xiang-Rui, and Lin, Chih-Jen. Liblinear: A library for large linear classification. Journal of Machine Learning Research, 9:1871-1874, 2008.
12. Fradkin, Dmitriy and Madigan, David. Experiments with random projections for machine learning. In KDD, pp. 517-522, Washington, DC, 2003.
13. Freund, Yoav, Dasgupta, Sanjoy, Kabra, Mayank, and Verma, Nakul. Learning the structure of manifolds using random projections. In NIPS, Vancouver, BC, Canada, 2008.
14. Friedman, Jerome H., Baskett, F., and Shustek, L. An algorithm for finding nearest neighbors. IEEE Transactions on Computers, 24:1000-1006, 1975.
15. Goemans, Michel X. and Williamson, David P. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of ACM, 42(6): 1115-1145, 1995.
16. Indyk, Piotr and Motwani, Rajeev. Approximate nearest neighbors: Towards removing the curse of dimensionality. In STOC, pp. 604-613, Dallas, TX, 1998.
17. Joachims, Thorsten. Training linear svms in linear time. In KDD, pp. 217-226, Pittsburgh, PA, 2006.
18. Johnson, William B. and Lindenstrauss, Joram. Extensions of Lipschitz mapping into Hilbert space. Contemporary Mathematics, 26:189-206, 1984.
19. Li, Ping. Very sparse stable random projections for dimension reduction in lα (0 < α ≤ 2) norm. In KDD, San Jose, CA, 2007.
20. Li, Ping, Hastie, Trevor J., and Church, Kenneth W. Improving random projections using marginal information. In COLT, pp. 635-649, Pittsburgh, PA, 2006.
21. Li, Ping, Owen, Art B, and Zhang, Cun-Hui. One permutation hashing. In NIPS, Lake Tahoe, NV, 2012.
22. Li, Ping, Mitzenmacher, Michael, and Shrivastava, Anshumali. Coding for random projections and approximate near neighbor search. Technical report, arXiv: 1403.8144, 2014.
23. Mitzenmacher, Michael and Vadhan, Salil. Why simple hash functions work: exploiting the entropy in a data stream. In SODA, 2008.
24. Nisan, Noam. Pseudorandom generators for space- bounded computations. In Proceedings of the twenty- second annual ACM symposium on Theory of computing, STOC, pp. 204-212, 1990.
25. Papadimitriou, Christos H., Raghavan, Prabhakar, Tamaki, Hisao, and Vempala, Santosh. Latent semantic indexing: A probabilistic analysis. In PODS, pp. 159-168, Seattle, WA, 1998.
26. Shalev-Shwartz, Shai, Singer, Yoram, and Srebro, Nathan. Pegasos: Primal estimated sub-gradient solver for svm. In ICML, pp. 807-814, Corvalis, Oregon, 2007.
27. Shrivastava, Anshumali and Li, Ping. Densifying one permutation hashing via rotation for fast near neighbor search. In ICML, Beijing, China, 2014.
28. Vempala, Santosh. The Random Projection Method. American Mathematical Society, Providence, RI, 2004.
29. Wang, Fei and Li, Ping. Efficient nonnegative matrix factorization with random projections. In SDM, 2010.
30. Weinberger, Kilian, Dasgupta, Anirban, Langford, John, Smola, Alex, and Attenberg, Josh. Feature hashing for large scale multitask learning. In ICML, pp. 1113-1120, 2009.