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.
|Title of host publication||Advances in Knowledge Discovery and Data Mining|
|Subtitle of host publication||PAKDD 2018|
|Editors||D. Phung, V. Tseng, G. Webb, B. Ho, M. Ganji, L. Rashidi|
|Place of Publication||Cham|
|Publication status||Published - 19 Jun 2018|
|Series||Lecture Notes in Computer Science|