Hashing for distributed data

32nd International Conference on Machine Learning, ICML 2015

Volumn 2, Issue , 2015, Pages 1642-1650

  Leng, Cong ^a   Wu, Jiaxiang ^a   Cheng, Jian ^a   Zhang, Xi ^a   Lu, Hanqing ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HASH FUNCTIONS; NEAREST NEIGHBOR SEARCH;

COMMUNICATION COST; COMPUTATION OVERHEADS; DISTRIBUTED DATA; FUSION CENTER; HASHING ALGORITHMS; LARGE-SCALE DATASETS; LEARNING PROCESS; TRAINING DATA;

LEARNING SYSTEMS;

EID: 84969802098     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Bertsekas, Dimitri P and Tsitsiklis, John N. Parallel and distributed computation: numerical methods, volume 23. Prentice hall Englewood Cliffs, NJ, 1989.
2. Boyd, Stephen, Parikh, Neal, Chu, Eric, Peleato, Borja, and Eckstein, Jonathan. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1): 1-122, 2011.
3. Charikar, M. Similarity estimation techniques from rounding algorithm. In ACM symposium on Theory of computing, pp. 380-388, 2002.
4. Cheng, Jian, Leng, Cong, Wu, Jiaxiang, Cui, Hainan, and Lu, Hanqing. Fast and accurate image matching with cascade hashing for 3d reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
5. Corbett, James C, Dean, Jeffrey, Epstein, Michael, Fikes, Andrew, Frost, Christopher, Furman, JJ, Ghemawat, Sanjay, Gubarev, Andrey, Heiser, Christopher, Hochschild, Peter, et al. Spanner: Googles globally distributed database. ACM Transactions on Computer Systems (TOCS), 31(3):8, 2013.
6. Datar, M., Immorlica, N., Indyk, P., and Mirrokni, V.S. Locality-sensitive hashing scheme based on p-stable distributions. In SCG, pp. 253-262, 2004.
7. Ge, Tiezheng, He, Kaiming, Ke, Qifa, and Sun, Jian. Optimized product quantization. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(4):744-755, April 2014.
8. Gong, Yunchao, Lazebnik, Svetlana, Gordo, Albert, and Per-ronnin, Florent. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35 (12):2916-2929, 2013.
9. Greenhill, Stewart and Venkatesh, Svetha. Distributed query processing for mobile surveillance. In Proceedings of the 15th international conference on Multimedia, pp. 413-422. ACM, 2007.
10. He, Kaiming, Wen, Fang, and Sun, Jian. K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In IEEE Conference on Computer Vision and Pattern Recognition, 2013.
11. Heo, J., Lee, Y., He, J., Chang, S., and Yoon, S. Spherical hashing. In IEEE Conference on Computer Vision and Pattern Recognition, 2012.
12. Hestenes, Magnus R. Multiplier and gradient methods. Journal of optimization theory and applications, 4(5):303-320, 1969.
13. Howard, Andrew, Matarić, Maja J, and Sukhatme, Gaurav S. Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In Distributed autonomous robotic systems 5, pp. 299-308. Springer, 2002.
14. Indyk, P. and Motwani, R. Approximate nearest neighbors: towards removing the curse of dimensionality. In STOC, 1998.
15. Jegou, Herve, Douze, Matthijs, and Schmid, Cordelia. Product quantization for nearest neighbor search. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(1): 117-128, 2011.
16. Kulis, B. and Grauman, K. Kernelized locality-sensitive hashing for scalable image search. In International Conference on Computer Vision, 2009.
17. Leng, Cong, Wu, Jiaxiang, Cheng, Jian, Bai, Xiao, and Lu, Hanqing. Online sketching hashing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
18. Liang, Junli, Zhang, Miaohua, Zeng, Xianyu, and Yu, Guoyang. Distributed dictionary learning for sparse representation in sensor networks. Image Processing, IEEE Transactions on, 23(6): 2528-2541, 2014.
19. Lin, Guosheng, Shen, Chunhua, Suter, David, and Hengel, Anton van den. A general two-step approach to learning-based hashing. In IEEE International Conference on Computer Vision (International Conference on Computer Vision), 2013.
20. Lin, Guosheng, Shen, Chunhua, Shi, Qinfeng, van den Hengel, Anton, and Suter, David. Fast supervised hashing with decision trees for high-dimensional data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1971-1978, 2014.
21. Liu, W., Wang, J., Kumar, S., and Chang, S. Hashing with graphs. In International Conference on Machine Learning, 2011.
22. Liu, W., Wang, J., Ji, R., Jiang, Y., and Chang, S. Supervised hashing with kernels. In IEEE Conference on Computer Vision and Pattern Recognition, 2012.
23. Norouzi, M. and Fleet, D.J. Cartesian k-means. In CVPR, pp. 3017-3024, 2013.
24. Oliva, Aude and Torralba, Antonio. Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision, 42(3):145-175, 2001.
25. Powell, Michael JD. A method for non-linear constraints in minimization problems. UKAEA, 1967.
26. Strecha, Christoph, Bronstein, Alexander M, Bronstein, Michael M, and Fua, Pascal. Ldahash: Improved matching with smaller descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(1):66-78, 2012.
27. Wang, J., Kumar, S., and Chang, S. Semi-supervised hashing for large-scale search. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(12):2393-2406, 2012.