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.
Original language | English (US) |
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Article number | 6338928 |
Pages (from-to) | 2381-2389 |
Number of pages | 9 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 25 |
Issue number | 10 |
DOIs | |
State | Published - 2013 |
Externally published | Yes |
Keywords
- Dimensionality reduction
- kernel machines
- multiple kernel learning
- subspace learning
- supervised learning
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics