Transfer learning is a method that studies how to identify the useful knowledge and skills in the previous tasks, and uses them to the new tasks or domains. At present, the research on transfer learning mostly focuses on the field of long texts. However, the source data should be given for the transportation from long texts to the short ones, and the priori probability distribution of the data should be given at the same time. In order to solve the problems, the algorithm which is called FSFP (Free Source selection Free Priori probability distribution) is proposed. It can transfer knowledge from the long texts to the short ones. Latent semantic analysis is used to extract the key words as seed characteristic sets, which are semantically related to the long texts from the target domain. And then the graph structure of online information is built. With the help of the improved Laplacian Eigenmaps, the feature representations of highdimensional data are mapped to a low-dimensional space. Lastly, the target data are classified in the constraint of minimizing the mutual information between the instance and the feature representation. The experimental results on large data sets show the effectiveness of the new algorithm.
Digital Object Identifier (DOI)
Fengmei, Wei; Jianpei, Zhang; Yan, Chu; and Jing, Yang
"FSFP: Transfer Learning From Long Texts to the Short,"
Applied Mathematics & Information Sciences: Vol. 08:
4, Article 62.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol08/iss4/62