A Research on Gaussian Process Based Feature Extraction

Zong Hai Sun; Osman Osman

doi:10.4028/www.scientific.net/AMM.300-301.848

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 300-301A Research on Gaussian Process Based Feature...

A Research on Gaussian Process Based Feature Extraction

Abstract:

Data sets of high–dimensional spaces are problematic when it comes to classification, compression, and visualization. The main issue is to find a reduced dimensionality representation that corresponds to the intrinsic dimensionality of the original data. In this paper we try to investigate a practical Bayesian method for feature extracting problem, in particular we will apply Gaussian Process Latent Variable Model (GPLVM) to a real world data set. Feature extraction experiments were performed on a cancer treatments’ components data set using GPLVM, then we used PCA on the same data set for comparison of the results.

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Periodical:

Applied Mechanics and Materials (Volumes 300-301)

Pages:

848-852

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.300-301.848

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Online since:

February 2013

Authors:

Zong Hai Sun, Osman Osman

Keywords:

Clustering, Feature Extraction, Gaussian Process

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