Sparse similarity-preserving hashing

2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings

Volumn , Issue , 2014, Pages

  Masci, Jonathan ^a   Bronstein, Alex M ^a   Bronstein, Michael M ^a   Sprechmann, Pablo ^a   Sapiro, Guillermo ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); DEGREES OF FREEDOM (MECHANICS); ECONOMIC AND SOCIAL EFFECTS; HASH FUNCTIONS; NEAREST NEIGHBOR SEARCH;

COMPUTATIONAL COSTS; EMBEDDING DIMENSIONALITY; EXPERIMENTAL EVALUATION; FALSE NEGATIVE RATE; FALSE POSITIVE RATES; HASHING TECHNIQUES; NUMBER OF DEGREES OF FREEDOM; SIMILARITY PRESERVING;

FEEDFORWARD NEURAL NETWORKS;

EID: 85083952582     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (39)

1. Belkin, M. and Niyogi, P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373–1396, 2003.
2. Bronstein, M. M. et al. Data fusion through cross-modality metric learning using similarity-sensitive hashing. In Proc. CVPR, 2010.
3. Chua, T.-S. et al. NUS-WIDE: A real-world web image database from national university of Singapore. In Proc. CIVR, 2009.
4. Coifman, R. R. and Lafon, S. Diffusion maps. App. Comp. Harmonic Analysis, 21(1):5–30, 2006.
5. Daubechies, I., Defrise, M., and De Mol, C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Comm. Pure and App. Math., 57 (11):1413–1457, 2004.
6. Davis et al. Information-theoretic metric learning. In Proc. ICML, 2007.
7. Gionis, A., Indyk, P., and Motwani, R. Similarity search in high dimensions via hashing. In Proc. VLDB, 1999.
8. Goemans, M. and Williamson, D. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM, 42(6):1115–1145, 1995.
9. Gong, Y. and Lazebnik, S. Iterative quantization: A pro-crustean approach to learning binary codes. In Proc. CVPR, 2011.
10. Gong, Y. et al. Angular quantization-based binary codes for fast similarity search. In Proc. NIPS, 2012.
11. Grauman, K. and Fergus, R. Learning binary hash codes for large-scale image search. In Machine Learning for Computer Vision, pp. 49–87. Springer, 2013.
12. Gregor, K. and LeCun, Y. Learning fast approximations of sparse coding. In ICML, 2010.
13. Hadsell, R., Chopra, S., and LeCun, Y. Dimensionality reduction by learning an invariant mapping. In Proc. CVPR, 2006.
14. Johnson, R. A. and Wichern, D. W. Applied multivariate statistical analysis, volume 4. Prentice Hall, 2002.
15. Korman, S. and Avidan, S. Coherency sensitive hashing. In Proc. ICCV, 2011.
16. Krizhevsky, Alex. Learning multiple layers of features from tiny images. Technical report, 2009.
17. Kulis, B. and Darrell, T. Learning to hash with binary reconstructive embeddings. In Proc. NIPS, 2009.
18. LeCun, Y. Une procédure d’apprentissage pour réseau à seuil asymétrique. Proceedings of Cognitiva 85, Paris, pp. 599–604, 1985.
19. Liu, W. et al. Hashing with graphs. In Proc. ICML, 2011.
20. Liu, Wei et al. Supervised hashing with kernels. In Proc. CVPR, 2012.
21. Masci, J. et al. Descriptor learning for omnidirectional image matching. Technical Report arXiv:1112.6291, 2011.
22. McFee, B. and Lanckriet, G. R. G. Partial order embedding with multiple kernels. In Proc. ICML, 2009.
23. Mika, S. et al. Fisher discriminant analysis with kernels. In Proc. Neural Networks for Signal Processing, 1999.
24. Norouzi, M. and Fleet, D. Minimal loss hashing for compact binary codes. In Proc. ICML, 2011.
25. Norouzi, M., Fleet, D., and Salakhutdinov, R. Hamming distance metric learning. In Proc. NIPS, 2012.
26. Roweis, S. T. and Saul, L. K. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500): 2323, 2000.
27. Schoelkopf, B., Smola, A., and Mueller, K. R. Kernel principal component analysis. Artificial Neural Networks, pp. 583–588, 1997.
28. Shakhnarovich, G., Viola, P., and Darrell, T. Fast pose estimation with parameter-sensitive hashing. In Proc. CVPR, 2003.
29. Shen, C. et al. Positive semidefinite metric learning with boosting. In Proc. NIPS, 2009.
30. Sprechmann, P., Bronstein, A. M., and Sapiro, G. Learning efficient sparse and low rank models. Technical Report arXiv:1010.3467, 2012.
31. Strecha, C. et al. LDAHash: Improved matching with smaller descriptors. PAMI, 34(1):66–78, 2012.
32. Taylor, G. W. et al. Learning invariance through imitation. In Proc. CVPR, 2011.
33. Torralba, A., Fergus, R., and Freeman, W. T. 80 million tiny images: A large data set for nonparametric object and scene recognition. PAMI, 30(11):1958–1970, 2008a.
34. Torralba, A., Fergus, R., and Weiss, Y. Small codes and large image databases for recognition. In Proc. CVPR, 2008b.
35. Wang, J., Kumar, S., and Chang, S.-F. Sequential projection learning for hashing with compact codes. In Proc. ICML, 2010.
36. Weinberger, K. Q. and Saul, L. K. Distance metric learning for large margin nearest neighbor classification. JMLR, 10:207–244, 2009.
37. Weiss, Y., Torralba, A., and Fergus, R. Spectral hashing. In Proc. NIPS, 2008.
38. Xing, E. P. et al. Distance metric learning with application to clustering with side-information. In Proc. NIPS, 2002.
39. Yagnik, J. et al. The power of comparative reasoning. In Proc. CVPR, 2011.