A probabilistic model for multimodal hash function learning

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 940-948

  Zhen, Yi ^a   Yeung, Dit Yan ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

binary latent factor models; hash function learning; metric learning; multimodal similarity search

Indexed keywords

EXPERIMENTAL VALIDATIONS; FUNCTION LEARNING; HAMMING SPACE; LATENT FACTOR; METRIC LEARNING; MULTI-MODAL; MULTI-MODAL DATA; MULTIPLE MODALITIES; PROBABILISTIC MODELS; REALISTIC DATA; SCALABILITY ISSUE; SIMILARITY SEARCH; SYNTHETIC DATA;

DATA MINING;

HASH FUNCTIONS;

EID: 84866037322     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339678     Document Type: Conference Paper

References (45)

1. A. Andoni. Nearest Neighbor Search: the Old, the New, and the Impossible. PhD thesis, Massachusetts Institute of Technology, 2009.
2. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications of the ACM, 51(1):117-122, 2008.
3. S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu. An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. Journal of the ACM, 45(6):891-923, 1998.
4. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
5. A. Z. Broder, M. Charikar, A. M. Frieze, and M. Mitzenmacher. Min-wise independent permutations. In STOC, 1998.
6. M. M. Bronstein, A. M. Bronstein, F. Michel, and N. Paragios. Data fusion through cross-modality metric learning using similarity-sensitive hashing. In CVPR, 2010.
7. M. Charikar. Similarity estimation techniques from rounding algorithms. In STOC, 2002.
8. T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y.-T. Zheng. NUS-WIDE: A real-world web image database from National University of Singapore. In CIVR, 2009.
9. A. Dasgupta, R. Kumar, and T. Sarlos. Fast locality-sensitive hashing. In KDD, 2011.
10. K. Eshghi and S. Rajaram. Locality sensitive hash functions based on concomitant rank order statistics. In KDD, 2008.
11. J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 3(3):209-226, 1977.
12. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In VLDB, 1999.
13. T. L. Griffiths and Z. Ghahramani. Infinite latent feature models and the Indian buffet process. In NIPS 18, 2005.
14. J. He, W. Liu, and S.-F. Chang. Scalable similarity search with optimized kernel hashing. In KDD, 2010.
15. K. A. Heller and Z. Ghahramani. A nonparametric Bayesian approach to modeling overlapping clusters. In AISTATS, 2007.
16. G. E. Hinton and R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-509, 2006.
17. P. Indyk and R. Motwani. Approximate nearest neighbors: Towards removing the curse of dimensionality. In STOC, 1998.
18. B. Kulis and T. Darrell. Learning to hash with binary reconstructive embeddings. In NIPS 22, 2009.
19. B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In ICCV, 2009.
20. S. Kumar and R. Udupa. Learning hash functions for cross-view similarity search. In IJCAI, 2011.
21. D. Lee, M. Hofmann, F. Steinke, Y. Altun, N. D. Cahill, and B. Schölkopf. Learning similarity measure for multi-modal 3D image registration. In CVPR, 2009.
22. R.-S. Lin, D. A. Ross, and J. Yagnik. SPEC hashing: Similarity preserving algorithm for entropy-based coding. In CVPR, 2010.
23. W. Liu, J. Wang, S. Kumar, and S.-F. Chang. Hashing with graphs. In ICML, 2011.
24. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
25. E. Meeds, Z. Ghahramani, R. Neal, and S. T. Roweis. Modeling dyadic data with binary latent factors. In NIPS 19, 2006.
26. Y. Mu, J. Shen, and S. Yan. Weakly-supervised hashing in kernel space. In CVPR, 2010.
27. M. Norouzi and D. J. Fleet. Minimal loss hashing for compact binary codes. In ICML, 2011.
28. N. Quadrianto and C. H. Lampert. Learning multi-view neighborhood preserving projections. In ICML, 2011.
29. M. Raginsky and S. Lazebnik. Locality-sensitive binary codes from shift-invariant kernels. In NIPS 22, 2009.
30. N. Rasiwasia, J. Costa Pereira, E. Coviello, G. Doyle, G. R. Lanckriet, R. Levy, and N. Vasconcelos. A new approach to cross-modal multimedia retrieval. In ACM MM, 2010.
31. R. Salakhutdinov and G. E. Hinton. Semantic hashing. In SIGIR Workshop on Information Retrieval and Applications of Graphical Models, 2007.
32. R. E. Schapire. A brief introduction to Boosting. In IJCAI, 1999.
33. G. Shakhnarovich. Learning Task-Specific Similarity. PhD thesis, Massachusetts Institute of Technology, 2005.
34. G. Shakhnarovich, T. Darrell, and P. Indyk, editors. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. MIT Press, March 2006.
35. G. Shakhnarovich, P. Viola, and T. Darrell. Fast pose estimation with parameter-sensitive hashing. In ICCV, 2003.
36. A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In CVPR, 2008.
37. F. Ture, T. Elsayed, and J. Lin. No free lunch: Brute force vs. locality-sensitive hashing for cross-lingual pairwise similarity. In SIGIR, 2011.
38. J. Wang, S. Kumar, and S.-F. Chang. Semi-supervised hashing for scalable image retrieval. In CVPR, 2010.
39. J. Wang, S. Kumar, and S.-F. Chang. Sequential projection learning for hashing with compact codes. In ICML, 2010.
40. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS 21, 2008.
41. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell. Distance metric learning with application to clustering with side-information. In NIPS 15, 2002.
42. H. Xu, J. Wang, Z. Li, G. Zeng, S. Li, and N. Yu. Complementary hashing for approximate nearest neighbor search. In ICCV, 2011.
43. D. Zhang, F. Wang, and L. Si. Composite hashing with multiple information sources. In SIGIR, 2011.
44. D. Zhang, J. Wang, D. Cai, and J. Lu. Self-taught hashing for fast similarity search. In SIGIR, 2010.
45. Y. Zhen and D.-Y. Yeung. Active hashing and its application to image and text retrieval. Data Mining and Knowledge Discovery, To appear.