Multiple kernel learning for dimensionality reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 33, Issue 6, 2011, Pages 1147-1160

  Lin, Yen Yu ^a   Liu, Tyng Luh ^a   Fuh, Chiou Shann ^b  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Dimensionality reduction; face recognition; image clustering; multiple kernel learning; object categorization

Indexed keywords

DATA REPRESENTATIONS; DESCRIPTORS; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION TECHNIQUES; HIGH-DIMENSIONAL; IMAGE CLUSTERING; IMAGE DESCRIPTORS; IMPROVING PERFORMANCE; MULTIPLE KERNEL LEARNING; OBJECT CATEGORIZATION; SEMI-SUPERVISED; SUPERVISED LEARNING PROBLEMS; VISUAL LEARNING;

OBJECT RECOGNITION;

FACE RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPUTER PROGRAM; FACE; HISTOLOGY; HUMAN; IMAGE PROCESSING; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; FACE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; SOFTWARE;

EID: 79955480657     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2010.183     Document Type: Article

References (59)

1. F. Bach, G. Lanckriet, and M. Jordan, "Multiple Kernel Learning, Conic Duality, and the SMO Algorithm," Proc. Int'l Conf. Machine Learning, 2004.
2. A. Berg, T. Berg, and J. Malik, "Shape Matching and Object Recognition Using Low Distortion Correspondences," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 26-33, 2005.
3. A. Berg and J. Malik, "Geometric Blur for Template Matching," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 607-614, 2001.
4. O. Boiman, E. Shechtman, and M. Irani, "In Defense of Nearest-Neighbor Based Image Classification," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
5. A. Bosch, A. Zisserman, and X. Muñoz, "Image Classification Using Random Forests and Ferns," Proc. IEEE Int'l Conf. Computer Vision, 2007.
6. D. Cai, X. He, and J. Han, "Semi-Supervised Discriminant Analysis," Proc. IEEE Int'l Conf. Computer Vision, 2007.
7. J. Carreira and C. Sminchisescu, "Constrained Parametric Min-Cuts for Automatic Object Segmentation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
8. C.-P. Chen and C.-S. Chen, "Lighting Normalization with Generic Intrinsic Illumination Subspace for Face Recognition," Proc. IEEE Int'l Conf. Computer Vision, pp. 1089-1096, 2005. (Pubitemid 44042302)
9. H.-T. Chen, H.-W. Chang, and T.-L. Liu, "Local Discriminant Embedding and Its Variants," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 846-853, 2005.
10. M. Christoudias, R. Urtasun, and T. Darrell, "Bayesian Localized Multiple Kernel Learning," technical report, Electical Eng. and Computer Science Dept., Univ. of California, Berkeley, 2009.
11. T. Cox and M. Cox, Multidimentional Scaling. Chapman & Hall, 1994.
12. D. Dueck and B. Frey, "Non-Metric Affinity Propagation for Unsupervised Image Categorization," Proc. IEEE Int'l Conf. Computer Vision, 2007.
13. M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge (VOC2007) Results, 2007.
14. L. Fei-Fei, R. Fergus, and P. Perona, "Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories," Proc. IEEE Computer Vision and Pattern Recognition Workshop Generative-Model Based Vision, 2004.
15. B. Frey and D. Dueck, "Clustering by Passing Messages between Data Points," Science, vol. 315, pp. 972-976, 2007. (Pubitemid 46281181)
16. A. Frome, Y. Singer, and J. Malik, "Image Retrieval and Classification Using Local Distance Functions," Advances in Neural Information Processing Systems, pp. 417-424, MIT Press, 2006.
17. A. Frome, Y. Singer, F. Sha, and J. Malik, "Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification," Proc. IEEE Int'l Conf. Computer Vision, 2007.
18. C. Galleguillos, B. McFee, S. Belongie, and G. Lanckriet, "Multi-Class Object Localization by Combining Local Contextual Interactions," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
19. P. Gehler and S. Nowozin, "On Feature Combination for Multi-class Object Classification," Proc. IEEE Int'l Conf. Computer Vision, 2009.
20. M. Gönen and E. Alpaydin, "Localized Multiple Kernel Learning," Proc. Int'l Conf. Machine Learning, pp. 352-359, 2008.
21. K. Grauman and T. Darrell, "The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features," Proc. IEEE Int'l Conf. Computer Vision, pp. 1458-1465, 2005. (Pubitemid 44042350)
22. R. Gross and V. Brajovic, "An Image Preprocessing Algorithm for Illumination Invariant Face Recognition," Proc. Int'l Conf. Audio-and Video-Based Biometric Person Authentication, pp. 10-18, 2003.
23. X. He and P. Niyogi, "Locality Preserving Projections," Advances in Neural Information Processing Systems, MIT Press, 2003.
24. A. Holub, M. Welling, and P. Perona, "Combining Generative Models and Fisher Kernels for Object Recognition," Proc. IEEE Int'l Conf. Computer Vision, pp. 136-143, 2005. (Pubitemid 44054951)
25. I. Joliffe, Principal Component Analysis. Springer-Verlag, 1986.
26. A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell, "Gaussian Processes for Object Categorization," Int'l J. Computer Vision, vol. 88, no. 2, pp. 169-188, 2010.
27. S.-J. Kim, A. Magnani, and S. Boyd, "Optimal Kernel Selection in Kernel Fisher Discriminant Analysis," Proc. Int'l Conf. Machine Learning, pp. 465-472, 2006.
28. A. Kumar and C. Sminchisescu, "Support Kernel Machines for Object Recognition," Proc. IEEE Int'l Conf. Computer Vision, 2007.
29. G. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, and M. Jordan, "Learning the Kernel Matrix with Semidefinite Programming," J. Machine Learning Research, vol. 5, pp. 27-72, 2004.
30. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2169-2178, 2006. (Pubitemid 44931582)
31. Y.-Y. Lin, T.-L. Liu, and C.-S. Fuh, "Local Ensemble Kernel Learning for Object Category Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
32. Y.-Y. Lin, T.-L. Liu, and C.-S. Fuh, "Dimensionality Reduction for Data in Multiple Feature Representations," Advances in Neural Information Processing Systems, pp. 961-968, MIT Press, 2008.
33. D. Lowe, "Distinctive Image Features from Scale-Invariant Key-points," Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
34. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R. Müller, "Fisher Discriminant Analysis with Kernels," Proc. Workshop Neural Networks for Signal Processing, pp. 41-48, 1999. (Pubitemid 30510358)
35. J. Mutch and D. Lowe, "Multiclass Object Recognition with Sparse, Localized Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 11-18, 2006. (Pubitemid 44931307)
36. T. Ojala, M. Pietikäinen, and T. Mäenpää, "Multiresolution GrayScale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, July 2002. (Pubitemid 34835471)
37. A. Oliva and A. Torralba, "Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope," Int'l J. Computer Vision, vol. 42, no. 3, pp. 145-175, 2001. (Pubitemid 32680801)
38. E. Pekalska, P. Paclik, and R. Duin, "A Generalized Kernel Approach to Dissimilarity-Based Classification," J. Machine Learning Research, vol. 2, no. 2, pp. 175-211, 2002.
39. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet, "More Efficiency in Multiple Kernel Learning," Proc. Int'l Conf. Machine Learning, pp. 775-782, 2007.
40. S. Roweis and L. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, pp. 2323-2326, 2000. (Pubitemid 32041578)
41. B. Schölkopf, A. Smola, and K.-R. Müller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol. 10, no. 5, pp. 1299-1319, 1998. (Pubitemid 128463674)
42. T. Serre, L. Wolf, and T. Poggio, "Object Recognition with Features Inspired by Visual Cortex," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 994-1000, 2005.
43. E. Shechtman and M. Irani, "Matching Local Self-Similarities across Images and Videos," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
44. T. Sim, S. Baker, and M. Bsat, "The CMU Pose, Illumination, and Expression Database," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1615-1618, Dec. 2003.
45. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, "Large Scale Multiple Kernel Learning," J. Machine Learning Research, vol. 7, pp. 1531-1565, 2006. (Pubitemid 44373694)
46. A. Strehl and J. Ghosh, "Cluster Ensembles-A Knowledge Reuse Framework for Combining Multiple Partitions," J. Machine Learning Research, vol. 3, pp. 583-617, 2002.
47. J. Tenenbaum, V. de Silva, and J. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, vol. 290, pp. 2319-2323, 2000. (Pubitemid 32041577)
48. S. Todorovic and N. Ahuja, "Learning Subcategory Relevances for Category Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
49. L. Vandenberghe and S. Boyd, "Semidefinite Programming," SIAM Rev., vol. 38, pp. 49-95, 1996. (Pubitemid 126610912)
50. M. Varma and D. Ray, "Learning the Discriminative Power-Invariance Trade-Off," Proc. IEEE Int'l Conf. Computer Vision, 2007.
51. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, "Multiple Kernels for Object Detection," Proc. IEEE Int'l Conf. Computer Vision, 2009.
52. H. Wang, S. Yan, D. Xu, X. Tang, and T. Huang, "Trace Ratio versus Ratio Trace for Dimensionality Reduction," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
53. M. Wu and B. Schölkopf, "A Local Learning Approach for Clustering," Advances in Neural Information Processing Systems, pp. 1529-1536, MIT Press, 2006.
54. S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin, "Graph Embedding and Extensions: A General Framework for Dimensionality Reduction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40-51, Jan. 2007. (Pubitemid 46415944)
55. J. Yang, Y. Li, Y. Tian, L. Duan, and W. Gao, "Group-Sensitive Multiple Kernel Learning for Object Categorization," Proc. IEEE Int'l Conf. Computer Vision, 2009.
56. J. Ye, R. Janardan, and Q. Li, "GPCA: An Efficient Dimension Reduction Scheme for Image Compression and Retrieval," Proc. ACM SIGKDD, pp. 354-363, 2004. (Pubitemid 40114945)
57. J. Ye, R. Janardan, and Q. Li, "Two-Dimensional Linear Discriminant Analysis," Advances in Neural Information Processing Systems, MIT Press, 2004.
58. H. Zhang, A. Berg, M. Maire, and J. Malik, "SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2126-2136, 2006. (Pubitemid 44931577)
59. J. Zhu, S. Rosset, H. Zou, and T. Hastie, "Multi-Class Adaboost," technical report, Dept. of Statistics, Univ. of Michigan, 2005.