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Last updated: Sept. 22 2013
If you have papers to recommend or any suggestions, please feel free to contact me.
Theory:
Corinna Cortes, Marius Kloft and Mehryar Mohri: Learning Kernels Using Local Rademacher Complexity. NIPS 2013.
Marius Kloft, Gilles Blanchard: The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning. NIPS 2011: 2438-2446
Zakria Hussain, John Shawe-Taylor:Improved Loss Bounds For Multiple Kernel Learning, JMLR - Proceedings Track 15: 370-377 (2011)
Zakria Hussain, John Shawe-Taylor: A note on Improved Loss Bounds For Multiple Kernel Learning
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh: Generalization Bounds for Learning Kernels. ICML 2010: 247-254
F. R. Bach, “Consistency of the group lasso and multiple kernel learning,” JMLR, vol. 9, pp. 1179–1225, 2008.
Nathan Srebro, Shai Ben-David: Learning Bounds for Support Vector Machines with Learned Kernels. COLT 2006: 169-183
G. R. G. Lanckriet, N. Cristianini, P. L. Bartlett, L. E. Ghaoui, and M. I. Jordan: Learning the kernel matrix with semidefinite programming, JMLR, vol. 5, pp. 27–72, 2004.
Algorithms:
Xinxing Xu, Ivor W. Tsang and Dong Xu: Soft Margin Multiple Kernel Learning. IEEE Trans. Neural Netw. Learning Syst., vol. 24, no. 5, pp. 749–761, 2013.
Xinxing Xu, Ivor W. Tsang and Dong Xu: Handling Ambiguity via Input-Output Kernel Learning, IEEE Int. Conf. on Data Mining (ICDM), December 2012, pp. 725-734.
F. Orabona, J. Luo, and B. Caputo, “Multi kernel learning with online batch optimization,” JMLR, vol. 13, pp. 227–253, 2012.
F. Orabona and J. Luo, “Ultra-fast optimization algorithm for sparse multi kernel learning,” in ICML, 2011.
T. Suzuki and R. Tomioka, “Spicymkl: a fast algorithm for multiple kernel learning with thousands of kernels,” Machine Learning, vol. 85, no. 1-2, pp. 77–108, 2011.
M. Kloft, U. Brefeld, S. Sonnenburg, and A. Zien: ℓp-norm multiple kernel learning, JMLR, vol. 12, pp. 953–997, 2011.
Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattacharyya, Jagarlapudi Saketha Nath, Sankaran Raman: Variable Sparsity Kernel Learning.JMLR 12: 565-592 (2011)
Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. Two-Layer Multiple Kernel Learning. AISTATS 2011, JMLR W & CPs, Vol. 15, Ft. Lauderdale, FL, USA, April 2011.
S. V. N. Vishwanathan, Z. Sun, N. Theera-Ampornpunt, and M. Varma,“Multiple kernel learning and the SMO algorithm,” in NIPS, 2010, pp. 2361–2369.
M. Kloft, U. R¨uckert, and P. L. Bartlett, “A unifying view of multiple kernel learning,” in ECML/PKDD, 2010.
C. Cortes, M. Mohri, and A. Rostamizadeh, “Two-stage learning kernel algorithms,” in International Conference Machine Learning, 2010, pp. 239–246.
Z. Xu, R. Jin, H. Yang, I. King, and M. R. Lyu: Simple and efficient multiple kernel learning by group lasso, in ICML, 2010.
M. Szafranski, Y. Grandvalet, and A. Rakotomamonjy: Composite kernel learning, Machine Learning, vol. 79, no. 1-2, pp. 73–103, 2010.
M. Varma and B. R. Babu, “More generality in efficient multiple kernel learning,” in International Conference on Machine Learning, 2009.
C. Cortes, M. Mohri, and A. Rostamizadeh: L2 regularization for learning kernels, in UAI, 2009.
J. Shawe-Taylor, “Kernel learning for novelty detection,” in NIPS 2008 Workshop Kernel Learning: Automatic Selection of Optimal Kernels.
Z. Xu, R. Jin, I. King, and M. R. Lyu: An extended level method for efficient multiple kernel learning, in NIPS, 2008.
A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet: Simplemkl, JMLR, vol. 9, pp. 2491–2512, 2008.
A. Zien and C. S. Ong, “Multiclass multiple kernel learning,” in International Conference on Machine Learning, 2007, pp. 1191–1198.
S. Sonnenburg, G. R¨atsch, C. Sch¨afer, and B. Sch¨olkopf: Large scale multiple kernel learning, JMLR, vol. 7, pp. 1531–1565, 2006.
F. R. Bach, R. Thibaux, and M. I. Jordan, “Computing regularization paths for learning multiple kernels,” in NIPS, 2004.
F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan: Multiple kernel learning, conic duality, and the SMO algorithm, in ICML, 2004.
G. R. G. Lanckriet, N. Cristianini, P. L. Bartlett, L. E. Ghaoui, and M. I. Jordan: Learning the kernel matrix with semidefinite programming, JMLR, vol. 5, pp. 27–72, 2004.
Applications:
For solving the ambiguous learning problems:
Wen Li, Lixin Duan, Dong Xu, Ivor W. Tsang. Learning with Augmented Features for Supervised and Semi-supervised Heterogeneous Domain Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Lin Chen, Lixin Duan, and Dong Xu, "Event Recognition in Videos by Learning From Heterogeneous Web Sources," in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2666-2673.
Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou. Convex and scalable weakly labeled SVMs. JMLR,14:2151-2188, Jul 2013
Xinxing Xu, Ivor W. Tsang and Dong Xu: Handling Ambiguity via Input-Output Kernel Learning, IEEE Int. Conf. on Data Mining (ICDM), December 2012, pp. 725-734.
Wen Li, Lixin Duan, Ivor, W.H. Tsang and Dong Xu: Co-Labeling: A New Multi-View Learning Approach for Ambiguous Problems, IEEE International Conference on Data Mning(ICDM), 2012.
Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong. Transfer Ordinal Label Learning. IEEE Transactions on Neural Networks and Learning Systems.
Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou. Tighter and Convex Maximum Margin Clustering. AISTATS 2009, JMLR W & CPs, Vol. 5, pp. 344-351.
For traditional supervised kernel learning problems:
Xinxing Xu, Ivor W. Tsang and Dong Xu: Soft Margin Multiple Kernel Learning. IEEE Trans. Neural Netw. Learning Syst., vol. 24, no. 5, pp. 749–761, 2013.
Serhat S. Bucak, Rong Jin, and Anil K. Jain, Multiple Kernel Learning for Visual Object Recognition: A Review. T-PAMI, 2013.
B. Gong, K. Grauman, and F. Sha. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation. Proceedings of the International Conference on Machine Learning (ICML), Atlanta, GA, June 2013.
Shengye Yan, Xinxing Xu, Dong Xu, Stephen Lin and Xuelong Li: Beyond Spatial Pyramids: A New Feature Extraction Framework with Dense Spatial Sampling for Image Classification, European Conference on Computer Vision (ECCV), 2012.
Lixin Duan, Ivor W. Tsang, Dong Xu. Domain Transfer Multiple Kernel Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):465-479, March 2012.
Niranjan Subrahmanya and Yung C. Shin: Sparse multiple kernel learning for signal processing applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 788-798, May 2010.
P. V. Gehler and S. Nowozin, “On feature combination for multiclass object classification,” in IEEE International Conference on Computer Vision, 2009, pp. 221–228.
C. Longworth and M. J. F. Gales, “Combining derivative and parametric kernels for speaker verification,” IEEE Transactions on Audio, Speech & Language Processing, vol. 17, no. 4, pp. 748–757, 2009.
M. Varma and D. Ray, “Learning the discriminative power-invariance trade-off,” in IEEE International Conference on Computer Vision, 2007, pp. 1–8.
Softwares:
LibMKL: SMMKL solver and Lp-MKL solver
GMKL: Lp-MKL solver
LevelMKL: elastic-net MKL solver
SpicyMKL: elastic-net MKL solver
UFOMKL: online MKL solver
SimpleMKL: L1-MKL solver
Data Sets: