Manifold mapping learning by regression tree boosting

Manifold learning has shown powerful information processing capability for high-dimensional data. In this paper, we proposed a manifold mapping learning algorithm to alleviate the shortage of traditional methods and broaden the applications of manifold learning. The mapping is achieved by using the...

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Bibliographic Details
Published in2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) pp. 1579 - 1583
Main Authors Xi'ai Chen, Zhi Han, Yandong Tang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Boosting
Face
Image reconstruction
Manifolds
Prediction algorithms
Regression tree analysis
Training data
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Summary:Manifold learning has shown powerful information processing capability for high-dimensional data. In this paper, we proposed a manifold mapping learning algorithm to alleviate the shortage of traditional methods and broaden the applications of manifold learning. The mapping is achieved by using the regression tree boosting, which is a strong ensemble learner composed by a group of regression trees as weak learners in the way of L 2 Boost. A set of verification experiments are conducted on both synthetic and real-world data sets. And the results have demonstrated that the algorithm can perform well on both regression and prediction applications.
ISBN:9781479987283
147998728X
DOI:10.1109/CYBER.2015.7288181