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Supervised Multiple Kernel Embedding for Learning Predictive Subspaces.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Oct2013, Vol. 25 Issue 10, p2381-2389. 9p. - Publication Year :
- 2013
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 25
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 89927400
- Full Text :
- https://doi.org/10.1109/TKDE.2012.213