Asymmetric Heterogeneous Transfer Learning: A Survey

Magda Friedjungová, Marcel Jiřina
2017 Proceedings of the 6th International Conference on Data Science, Technology and Applications  
One of the main prerequisites in most machine learning and data mining tasks is that all available data originates from the same domain. In practice, we often can't meet this requirement due to poor quality, unavailable data or missing data attributes (new task, e.g. cold-start problem). A possible solution can be the combination of data from different domains represented by different feature spaces, which relate to the same task. We can also transfer the knowledge from a different but related
more » ... ask that has been learned already. Such a solution is called transfer learning and it is very helpful in cases where collecting data is expensive, difficult or impossible. This overview focuses on the current progress in the new and unique area of transfer learning -asymmetric heterogeneous transfer learning. This type of transfer learning considers the same task solved using data from different feature spaces. Through suitable mappings between these different feature spaces we can get more data for solving data mining tasks. We discuss approaches and methods for solving this type of transfer learning tasks. Furthermore, we mention the most used metrics and the possibility of using metric or similarity learning.
doi:10.5220/0006396700170027 dblp:conf/data/FriedjungovaJ17 fatcat:xxogibknanf6rkj65cv5en7v5e