This paper introduces a novel classification algorithm for heterogeneous domain adaptation. The algorithm projects both the target and source data into a common feature space of the class decomposition scheme used. The distinctive features of the algorithm are: (1) it does not impose any assumptions on the data other than sharing the same class labels; (2) it allows adaptation of multiple source domains at once; and (3) it can help improving the topology of the projected data for class separability. The algorithm provides two built-in classification rules and allows applying any other classification model.
CITATION STYLE
Ismailoglu, F., Smirnov, E., Peeters, R., Zhou, S., & Collins, P. (2018). Heterogeneous domain adaptation based on class decomposition schemes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10937 LNAI, pp. 169–182). Springer Verlag. https://doi.org/10.1007/978-3-319-93034-3_14
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