1. Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction.
- Author
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Gao, Xinbo, Wang, Xiumei, Tao, Dacheng, and Li, Xuelong
- Subjects
SUPERVISED learning ,GAUSSIAN processes ,MATHEMATICAL variables ,DIMENSION reduction (Statistics) ,PROBABILITY theory ,MACHINE learning ,PRINCIPAL components analysis - Abstract
The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e.g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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