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Supervised Multiple Kernel Embedding for Learning Predictive Subspaces
Oct. 2013 (vol. 25 no. 10)
pp. 2381-2389
Mehmet Gonen, Aalto University School of Science, Espoo
For supervised learning problems, dimensionality reduction is generally applied as a preprocessing step. However, coupled training of dimensionality reduction and supervised learning steps may improve the prediction performance. In this paper, we propose a novel dimensionality reduction algorithm coupled with a supervised kernel-based learner, called supervised multiple kernel embedding, that integrates multiple kernel learning to dimensionality reduction and performs prediction on the projected subspace with a joint optimization framework. Combining multiple kernels allows us to combine different feature representations and/or similarity measures toward a unified subspace. We perform experiments on one digit recognition and two bioinformatics data sets. Our proposed method significantly outperforms multiple kernel Fisher discriminant analysis followed by a standard kernel-based learner, especially on low dimensions.
Index Terms:
Kernel,Training,Optimization,Vectors,Supervised learning,Support vector machines,Standards,subspace learning,Kernel,Training,Optimization,Vectors,Supervised learning,Support vector machines,Standards,supervised learning,Dimensionality reduction,kernel machines,multiple kernel learning
Citation:
Mehmet Gonen, "Supervised Multiple Kernel Embedding for Learning Predictive Subspaces," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2381-2389, Oct. 2013, doi:10.1109/TKDE.2012.213
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