Active hashing and its application to image and text retrieval

Data Mining and Knowledge Discovery

Volumn 26, Issue 2, 2013, Pages 255-274

  Zhen, Yi ^a   Yeung, Dit Yan ^a  

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

Author keywords

Active hashing; Hash function learning; Hashing; Semi supervised hashing

Indexed keywords

ACTIVE HASHING; BATCH MODES; BENCHMARK DATA; DATA UNCERTAINTY; FUNCTION LEARNING; HASHING; HASHING METHOD; INFORMATIVENESS; MACHINE LEARNING COMMUNITIES; SEMANTIC SIMILARITY; SEMI-SUPERVISED; SIMILARITY SEARCH; TEXT RETRIEVAL;

COMPUTER APPLICATIONS; DATA MINING;

HASH FUNCTIONS;

EID: 84872416414     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-012-0249-y     Document Type: Article

References (44)

1. Andoni A, Indyk P (2006) Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: Proceedings of the 47th annual IEEE symposium on foundations of computer science, FOCS '06, IEEE Computer Society, Washington, pp 459-468
2. Angluin D (1988) Queries and concept learning. Mach Learn 2(4): 319-342
3. Arya S, Mount DM, Netanyahu NS, Silverman R, Wu AY (1998) An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J ACM 45(6): 891-923
4. Atkinson AC, Donev A (1992) Optimum experimental designs. Oxford University Press, New York, NY
5. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge, UK
6. Bronstein MM, Bronstein AM, Michel F, Paragios N (2010) Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: Proceedings of IEEE conference on computer vision and pattern recognition [46], pp 3594-3601
7. Cohn D, Atlas L, Ladner R (1994) Improving generalization with active learning. Mach Learn 15(2): 201-221
8. Eshghi K, Rajaram S (2008) Locality sensitive hash functions based on concomitant rank order statistics. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, KDD '08, ACM, New York, pp 221-229
9. Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4: 933-969
10. Friedman JH, Bentley JL, Finkel RA (1977) An algorithm for finding best matches in logarithmic expected time. ACM Transac Math Softw 3(3): 209-226
11. Guo Y, Greiner R (2007) Optimistic active learning using mutual information. In: Veloso MM (ed) Proceedings of the 20th international joint conference on artificial intelligence, IJCAI '07, pp 823-829
12. Guo Y, Schuurmans D (2007) Discriminative batch mode active learning. In: Platt JC, Koller D, Singer Y, Roweis S (eds), Advances in neural information processing systems 20, NIPS 20, The MIT Press, Cambridge, MA, pp 593-600
13. He J, Liu W, Chang S-F (2010) Scalable similarity search with optimized kernel hashing. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, KDD '10, ACM, New York, pp 1129-1138
14. He X, Min W, Cai D, Zhou K (2007) Laplacian optimal design for image retrieval. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR '07, ACM, New York, pp 119-126
15. Hoi SCH, Jin R, Zhu J, Lyu MR (2006a) Batch mode active learning and its application to medical image classification. In: Proceedings of the 23rd international conference on machine learning [45], pp 417-424
16. Hoi SCH, Jin R, Lyu MR (2006b) Large-scale text categorization by batch mode active learning. In: Proceedings of the 15th international conference on world wide web, WWW '06, ACM, New York, pp 633-642
17. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, KDD '08, ACM, New York, pp 426-434
18. Kulis B, Darrell T (2009) Learning to hash with binary reconstructive embeddings. In: Bengio Y, Schuurmans D, Lafferty J, Williams CKI, Culotta A (eds) Advances in neural information processing systems 22, NIPS 22, The MIT Press, Cambridge, MA, pp 1042-1050
19. Kulis B, Grauman K (2009) Kernelized locality-sensitive hashing for scalable image search. In: Proceedings of IEEE 12th international conference on computer vision, ICCV '09, IEEE Computer Society, Washington, pp 2130-2137
20. Lewis DD, Gale WA (1994) A sequential algorithm for training text classifiers. In Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR '94, Springer-Verlag New York, Inc., New York, pp 3-12
21. Lin R-S, Ross DA, Yagnik J (2010) SPEC hashing: similarity preserving algorithm for entropy-based coding. In: Proceedings of IEEE conference on computer vision and pattern recognition [46], pp 848-854
22. MacKay DJC (1992) Information-based objective functions for active data selection. Neural Comput 4(4): 590-604
23. McCallum A, Nigam K (1998) Employing EM and pool-based active learning for text classification. In: Proceedings of the 15th international conference on machine learning, ICML '98, Morgan Kaufmann Publishers Inc., San Francisco, pp 350-358
24. Mu Y, Shen J, Yan S (2010) Weakly-supervised hashing in kernel space. In: Proceedings of IEEE conference on computer vision and pattern recognition [46], pp 3344-3351
25. Mu Y, Yan S (2010) Non-metric locality-sensitive hashing. In: Fox M, Poolev (eds) Proceedings of the 24th AAAI conference on artificial intelligence, AAAI '10, AAAI Press, Menlo Park, CA, pp 539-544
26. Nguyen HT, Smeulders A (2004) Active learning using pre-clustering. In:Proceedings of the 21st international conference on machine learning, ICML '04, ACM, New York, pp 79-86
27. Nicholas R, McCallum A (2001) Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the 18th international conference on machine learning, ICML '01, Morgan Kaufmann Publishers Inc., San Francisco, pp 441-448
28. Salakhutdinov R, Hinton GE (2009) Semantic hashing. Int J Approx Reason 50: 969-978
29. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5): 513-523
30. Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1): 1-47
31. Seung HS, Opper M, Sompolinsky H (1992) Query by committee. In: Proceedings of the 5th annual workshop on computational learning theory, COLT '92, ACM, New York, pp 287-294
32. Shakhnarovich G (2005) Learning task-specific similarity. PhD thesis, Massachusetts Institute of Technology
33. Shakhnarovich G, Darrell T, Indyk P (2006) Nearest-neighbor methods in learning and vision: theory and practice. The MIT Press, Cambridge, MA
34. Tong S, Koller D (2002) Support vector machine active learning with applications to text classification. J Mach Learn Res 2: 45-66
35. Torralba A, Fergus R, Weiss Y (2008) Small codes and large image databases for recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, CVPR '08, IEEE Computer Society, Los Alamitos, pp 1-8
36. Wang J, Kumar S, Chang S-F (2010a) Semi-supervised hashing for scalable image retrieval. In: Proceedings of IEEE conference on computer vision and pattern recognition [46], pp 3424-3431
37. Wang J, Kumar S, Chang S-F (2010b) Sequential projection learning for hashing with compact codes. In: Proceedings of the 27th international conference on machine learning, ICML '10, Omnipress, Haifa, pp 1127-1134
38. Weiss Y, Torralba A, Fergus R (2008) Spectral hashing. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems 21, NIPS 21, The MIT Press, Cambridge, MA, pp 1753-1760
39. Yianilos PN (1993) Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the 4th annual ACM-SIAM symposium on discrete algorithms, SODA '93, Society for Industrial and Applied Mathematics, Philadelphia, pp 311-321
40. Yu K, Bi J, Tresp V (2006) Active learning via transductive experimental design. In: Proceedings of the 23rd international conference on machine learning [47], pp 1081-1088
41. Yu K, Zhu S, Xu W, Gong Y (2008) Non-greedy active learning for text categorization using convex transductive experimental design. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, SIGIR '08, ACM, New York, pp 635-642
42. Zhang D, Wang J, Cai D, Lu J (2010) Self-taught hashing for fast similarity search. In: Proceedings of the 33rd annual international ACM SIGIR conference on research and development in information retrieval [47], pp 18-25
43. Zhen Y, Yeung D-Y (2010) Supervised experimental design and its application to text retrieval. In: Proceedings of the 33rd annual international ACM SIGIR conference on research and development in information retrieval [47], pp 299-306
44. Zhu X, Lafferty J, Ghahramani Z (2003) Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. In: ICML workshop on the continuum from labeled to unlabeled data in machine learning and data mining, ICML '03