Abstract
Various research efforts have been invested in machine learning and data mining for finding out patterns from data. However, even when some knowledge may be learned in one domain, it is often difficult to re-use it for another domain with different characteristics. Toward effective knowledge transfer between domains, we proposed a transfer learning method based on our transfer hypothesis that two domains have similar feature spaces. A graph structure called a topic graph is constructed by using the learned features in one domain, and the graph is used as a regularization term. In this paper we present a theoretical analysis of our approach and prove the convergence of the learning algorithm. Furthermore, the performance evaluation of the method is reported over document clustering problems. Extensive experiments are conducted to compare with other transfer learning algorithms. The results are encouraging, and show that our method can improve the performance by transferring the learned knowledge effectively.
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Yoshida, T., Ogino, H. (2013). Theoretical Analysis and Evaluation of Topic Graph Based Transfer Learning. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds) Active Media Technology. AMT 2013. Lecture Notes in Computer Science, vol 8210. Springer, Cham. https://doi.org/10.1007/978-3-319-02750-0_11
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DOI: https://doi.org/10.1007/978-3-319-02750-0_11
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