Regression transfer learning based on principal curve

Authors:
Wentao Mao;Guirong Yan;Junqing Bai;Hao Li
Affiliations:
The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China;The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China;The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China;The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

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Abstract

One of the basic ideas of present transfer learning algorithms is to find a common underlying representation among multiple related tasks as a bridge of transferring knowledge Different with most transfer learning algorithms which are designed to solve classification problems, a new algorithm is proposed in this paper to solve multiple regression tasks First, based on ”self-consistency” of principal curves, this algorithm utilizes non-parametric approach to find the principal curve passing through data sets of all tasks We treat this curve as common-across-tasks representation Second, the importance of every sample in target task is determined by computing the deviation from the principal curve and finally the weighted support vector regression is used to obtain a regression model We simulate multiple related regression tasks using noisy Sinc data sets with various intensities and report experiments which demonstrate that the proposed algorithm can draw the useful information of multiple tasks and dramatically improve the performance relative to learning target task independently Furthermore, we replace principal curve with support vector regression with model selection to find common representation and show the comparative results of these two algorithms.