A robust and efficient doubly regularized metric learning approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7575 LNCS, Issue PART 4, 2012, Pages 646-659

  Liu, Meizhu ^a   Vemuri, Baba C ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

boosting; PSD matrix; Regularized metric learning

Indexed keywords

BOOSTING; DATA SETS; DISTANCE METRICS; LINEAR COMBINATIONS; MAHALANOBIS DISTANCES; METRIC LEARNING; NON-SMOOTH; PARAMETERIZED; POSITIVE SEMIDEFINITE MATRICES; TRAINING EXAMPLE; TRAINING SAMPLE; WEIGHT CHANGE;

FACE RECOGNITION; LEARNING ALGORITHMS;

COMPUTER VISION;

EID: 84867882058     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33765-9_46     Document Type: Conference Paper

References (39)

1. Bi, J., et al.: AdaBoost on low-rank PSD matrices for metric learning with applications in Computer Aided Diagnosis. In: IEEE CVPR, pp. 1049-1056 (2011)
2. Yang, L., et al.: A Boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval. TPAMI, 30-44 (2010)
3. Ong, E., Bowden, R.: A boosted classifier tree for hand shape detection. In: IEEE Int. Conf. Automatic Face & Gesture Recogn., pp. 889-894 (2004)
4. Cao, Z., Yin, Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. In: IEEE CVPR, pp. 2707-2714 (2010)
5. Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi-supervised learning for image classification. In: IEEE CVPR, pp. 902-909 (2010)
6. Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: IEEE ICCV, pp. 365-372 (2009)
7. Nowak, E., Jurie, F.: Learning visual similarity measures for comparing never seen objects. In: IEEE CVPR (2007)
8. Pinto, N., DiCarlo, J.J., Cox, D.D.: How far can you get with a modern face recognition test set using only simple features? In: IEEE CVPR (2009)
9. Shen, C., Kim, J., Wang, L.: A scalable dual approach to semidefinite metric learning. In: IEEE CVPR, pp. 2601-2608 (2011)
10. Jiang, N., Liu, W., Wu, Y.: Adaptive and Discriminative Metric Differential Tracking. In: IEEE CVPR (2011)
11. Xing, E., Ng, A., Jordan, M., Russell, S.: Distance metric learning, with application to clustering with side-information. NIPS 15, 505-512 (2002)
12. Shen, C., Kim, J., Wang, L., van den Hengel, A.: Positive semidefinite metric learning with boosting. In: NIPS (2009)
13. Bar-Hillel, A., Hertz, T., Shental, N.,Weinshall, D.: Learning a mahalanobis metric from equivalence constraints. JMLR 6, 937-965 (2005)
14. Davis, J., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: ICML, pp. 209-216 (2007)
15. Cox, M., Cox, T.: Multidimensional Scaling. In: Handbooks Comp. Statistics, pp. 315-347. Springer (2008)
16. Tenenbaum, J., Silva, V., Langford, J.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319-2323 (2000)
17. Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323-2326 (2000)
18. Vemuri, B.C., Liu, M., Amari, S.I., Nielsen, F.: Total Bregman divergence and its applications to DTI analysis. IEEE TMI 30, 475-483 (2011)
19. Shalev-Shwartz, S., Singer, Y., Ng, A.Y.: Online and batch learning of pseudometrics. In: ICML (2004)
20. Tsuda, K., Rsch, G., Warmuth, M.K.: Matrix exponentiated gradient updates for on-line learning and bregman projection. JMLR 6, 995-1018 (2005)
21. Jian, B., Vemuri, B.C.: Metric learning using iwasawa decomposition. In: IEEE ICCV, pp. 1-6 (2007)
22. Saberian, M.J., Vasconcelos, N.: Multiclass Boosting: Theory and Algorithms. In: NIPS (2011)
23. Liu, M., Vemuri, B.C.: Robust and efficient regularized boosting using total bregman divergence. In: IEEE CVPR, pp. 2897-2902 (2011)
24. Warmuth, M.K., Glocer, K.A., Vishwanathan, S.V.: Entropy regularized LPBoost. In: Int. Conf. Alg. Learn. Theory, pp. 256-271 (2008)
25. Liu, M., , Vemuri, B.C., Amari, S., Nielsen, F.: Shape retrieval using hierarchical total bregman soft clustering. IEEE TPAMI (2012)
26. Boyd, S., Vandenberghe, L.: Convex optimization. Cambridge University Press, Cambridge (2004)
27. Demiriz, A., Bennett, K.P., Shawe-Taylor, J.: Linear programming boosting via column generation. Mach. Learn. 46, 225-254 (2002)
28. Schapire, R., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37, 297-336 (1999)
29. Frank, A., Asuncion, A.: UCI machine learning repository (2010)
30. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. In: ECCV (2008)
31. French, J., Watson, J., Jin, X., Martin, W.: An exogenous approach for adding multiple image representations to content-based image retrieval systems. In: Int. Sym. Signal Processing App., vol. 1, pp. 201-204 (2003)
32. Canny, J.: A computational approach to edge detection. IEEE TPAMI 8, 679-698 (1986)
33. Manjunath, B.S., Ma, W.: Texture features for browsing and retrieval of image data. IEEE TPAMI 18, 837-842 (1996)
34. Nguyen, H.V., Bai, L.: Cosine Similarity Metric Learning for Face Verification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part II. LNCS, vol. 6493, pp. 709-720. Springer, Heidelberg (2011)
35. Yin, Q., Tang, X., Sun, J.: An Associate-Predict Model for Face Recognition. In: IEEE CVPR, pp. 497-504 (2011)
36. Taigman, Y., Wolf, L., Hassner, T., Tel-Aviv, I.: Multiple One-Shots for utilizing class label information. In: BMVC (2009)
37. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91-110 (2004)
38. Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? metric learning approaches for face identification. In: IEEE ICCV, pp. 498-505 (2009)
39. Wolf, L., Hassner, T., Taigman, Y.: Similarity Scores Based on Background Samples. In: Zha, H., Taniguchi, R.-I., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp. 88-97. Springer, Heidelberg (2010)