CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2008 vol.30 Issue No.02 - February

Subscribe

Issue No.02 - February (2008 vol.30)

pp: 348-353

ABSTRACT

With the newly-proposed Canonical Correlation Analysis (CCA) named NmCCA that is an alternative formulation of CCA for more than two views of the same phenomenon, we develop a new effective multiple kernel learning algorithm. First, we adopt the empirical kernels to map the input data into m different feature spaces corresponding to different kernels. Then through the incorporation of NmCCA in a learning algorithm, one single learning process based on the regularization learning is developed, where a special term called Inter-Function Similarity Loss RIFSL is introduced for the agreement of multi-view outputs. In implementation, we select the Modification of Ho-Kashyap algorithm with Squared approximation of the misclassification errors (MHKS) as the incorporated paradigm, and the experimental results on benchmark datasets demonstrate the feasibility and effectiveness of the proposed algorithm named MultiK-MHKS.

INDEX TERMS

Multiple kernel learning, Canonical correlation analysis, Modified Ho-Kashyap algorithm, Single learning process, Pattern recognition

CITATION

Zhe Wang, Songcan Chen, Tingkai Sun, "MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.30, no. 2, pp. 348-353, February 2008, doi:10.1109/TPAMI.2007.70786REFERENCES

- [1] F. Bach, G.R.G. Lanckriet, and M.I. Jordan, “Multiple Kernel Learning, Conic Duality, and the SMO Algorithm,”
Proc. 21st Int'l Conf. Machine Learning, 2004.- [2] K.P. Bennett, M. Momma, and M.J. Embrechts, “MARK: A Boosting Algorithm for Heterogeneous Kernel Models,”
Proc. ACM SIGKDD, pp. 24-31, 2002.- [4] O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing Multiple Parameters for Support Vector Machines,”
Machine Learning, vol. 46, no. 1-3, pp. 131-159, 2002.- [6] N. Cristianini and J. Shawe-Taylor,
An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge Univ. Press, 2000.- [7] I.M. de Diego, J.M. Moguerza, and A. Muñoz, “Combining Kernel Information for Support Vector Classification,”
Proc. Fifth Int'l Workshop Multiple Classifier Systems, pp. 102-111, 2004.- [8] I.M. de Diego, J.M. Moguerza, and A. Muñoz, “On the Fusion of Polynomial Kernels for Support Vector Classifiers,”
Proc. Int'l Conf. Intelligent Data Eng. and Automated Learning, pp. 330-337, 2006.- [9] J.D.R. Farquhar, D.R. Hardoon, H. Meng, J. Shawe-Taylor, and S. Szedmak, “Two View Learning: SVM-2K, Theory and Practice,”
Neural Information Processing Systems, 2005.- [10] Y. Grandvalet and S. Canu, “Adaptive Scaling for Feature Selection in SVMs,”
Neural Information Processing Systems, 2002.- [12] H. Hotelling, “Relations between Two Sets of Variates,”
Biometrika, vol. 28, pp. 321-377, 1936.- [13] U. Kreßel, “Pairwise Classification and Support Vector Machines,”
Advances in Kernel Methods: Support Vector Machine, B. Schölkopf, C. Burges, A. Somla, eds., pp. 255-268, MIT Press, 1998.- [15] G.R.G. Lanckriet, N. Cristianini, P. Bartlett, L.E. Ghaoui, and M.I. Jordan, “Learning the Kernel Matrix with Semidefinite Programming,”
J. Machine Learning Research, vol. 5, pp. 27-72, 2004.- [17] Y. Li and J. Shawe-Taylor, “Using KCCA for Japanese-English Cross-Language Information Retrieval and Classification,”
J. Intelligent Information Systems, 2005.- [19] T.M. Mitchell,
Machine Learning. McGraw-Hill, 1997.- [20] J.M. Moguerza, A. Muñoz, and I.M. de Diego, “Fusion of Gaussian Kernels within Support Vector Classification,”
Proc. Iberoamerican Congress on Pattern Recognition, pp. 945-953, 2006.- [21] M. Momma and K. Bennett, “A Pattern Search Method for Model Selection of Support Vector Regression,”
Proc. Second SIAM Int'l Conf. Data Mining, pp. 261-274, 2002.- [23] C.S. Ong, A.J. Smola, and R.C. Williamson, “Learning the Kernel with Hyperkernels,”
J. Machine Learning Research, vol. 6, pp. 1043-1071, 2005.- [24] E. Pekalska, P. Paclik, and R.P.W. Duin, “A Generalized Kernel Approach to Dissimilarity-Based Classification,”
J. Machine Learning Research, vol. 2, pp. 175-211, 2001.- [26] J. Shawe-Taylor and N. Cristianini,
Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.- [27] S. Sonnenburg, G. Rätsch, and C. Schäfer, “A General and Efficient Multiple Kernel Learning Algorithm,”
Neural Information Processing Systems, 2005.- [28] S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, “Large Scale Multiple Kernel Learning,”
J. Machine Learning Research, 2006.- [29] I. Tsang, A. Kocsor, and J. Kwok, “Efficient Kernel Feature Extraction for Massive Data Sets,”
Proc. Int'l Conf. Knowledge Discovery and Data Mining, 2006.- [30] V. Vapnik,
Statistical Learning Theory. John Wiley & Sons, 1998. |