Abstract
Transfer learning is a widely investigated learning paradigm that is initially proposed to reuse informative knowledge from related domains, as supervised information in the target domain is scarce while it is sufficiently available in the multiple source domains. One of the challenging issues in transfer learning is how to handle the distribution differences between the source domains and the target domain. Most studies in the research field implicitly assume that data distributions from the source domains and the target domain are similar in a well-designed feature space. However, it is often the case that label assignments for data in the source domains and the target domain are significantly different. Therefore, in reality even if the distribution difference between a source domain and a target domain is reduced, the knowledge from multiple source domains is not well transferred to the target domain unless the label information is carefully considered. In addition, noisy data often emerge in real world applications. Therefore, considering how to handle noisy data in the transfer learning setting is a challenging problem, as noisy data inevitably cause a side effect during the knowledge transfer. Due to the above reasons, in this paper, we are motivated to propose a robust framework against noise in the transfer learning setting. We also explicitly consider the difference in data distributions and label assignments among multiple source domains and the target domain. Experimental results on one synthetic data set, three UCI data sets and one real world text data set in different noise levels demonstrate the effectiveness of our method.
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Appendix
Appendix
1.1 A.1 DBSCAN algorithm
The DBSCAN algorithm is proposed by Ester et al. (1996). A summary of the DBSCAN algorithm is shown in Algorithm 4.
1.2 A.2 Multidimensional scale (MDS) algorithm
Multidimensional scaling is a statistical technique to visualize the dissimilarity in data (Schiffman et al. 1981). We summarized the MDS algorithm in Algorithm 6. Given a matrix of pair-wise distances, MDS computes the coordinates for the data. Subsequently, the algorithm performs an eigen-decomposition of the data, and then the top d eigenvectors of the distance matrix are selected to represent the coordinates in the new d-dimensional Euclidean space.
1.3 A.3 Results in table format
This section contains results in table formats. By results in tables, readers have more detail information.
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Huy, T.N., Tong, B., Shao, H. et al. Transfer learning by centroid pivoted mapping in noisy environment. J Intell Inf Syst 41, 39–60 (2013). https://doi.org/10.1007/s10844-012-0226-3
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DOI: https://doi.org/10.1007/s10844-012-0226-3