Locality-sensitive support vector machine by exploring local correlation and global regularization

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 841-848

  Qi, Guo Jun ^a   Tian, Qi ^b   Huang, Thomas ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b One UTSA Circle   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION; SUPPORT VECTOR MACHINES;

CLASSIFICATION TASKS; DISCRIMINATING ABILITIES; LOCAL CLASSIFIER; LOCAL CORRELATIONS; LOCALITY SENSITIVE HASHING; LOCALITY SENSITIVES; REAL WORLD WEB; SAMPLE DISTRIBUTIONS;

IMAGE RETRIEVAL;

EID: 80052912052     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995378     Document Type: Conference Paper

References (15)

1. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications of the ACM, (1), January 2008. 842, 847
2. L. Bottou and V. Vapnik. Local learning algorithms. Neural Computation, (6):888-900, November 1992. 842, 843
3. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. 845
4. H. Cheng, P.-N. Tan, and R. Jin. Localized support vector machine and its efficient algorithm. In Proc of SIAM Int'l Conf. on Data Mining, 2007. 842, 846
5. 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 Proc. ACM CIVR, 2009. 846
6. O. Chum, M. Perd'och, and J. Matas. Geometric minhashing: Finding a (thick) needle in a haystack. In Proc. IEEE CVPR, 2009. 841
7. M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In Proc. ACM SCG, Brooklyn, New York, June 9-11 2004. 842
8. T. Evgeniou and M. Pontil. Regularized multi-task learning. In Proc. SIGKDD, 2004. 845
9. J. A. Hanley and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143:29-36, 1982. 847
10. T. K. Ho and E. M. Kleinberg. Building projectable classifiers of arbitrary complexity. In Proc. ICPR, 1996. 846
11. T. Mitchell. Machine Learning, chapter Computational Learning Theory. McGraw Hill, 1997. 845
12. G.-J. Qi, X.-S. Hua, and H.-J. Zhang. Learning semantic distance from community-tagged media collection. In Proc. ACM Multimedia, 2009. 841
13. V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, Inc, 1998. 844, 846
14. Z. Wu, Q. Ke, M. Isard, and J. Sun. Bundling features for large scale partial-duplicate web image search. In Proc. IEEE CVPR, 2009. 841
15. H. Zhang, A. C. Berg, M. Maire, and J. Malik. Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In Proc. IEEE CVPR, 2006. 842, 846