Issue No. 10 - Oct. (2013 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.213
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.
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
M. Gonen, "Supervised Multiple Kernel Embedding for Learning Predictive Subspaces," in IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 2381-2389, 2013.