Ismailoglu, FiratSmirnov, EvgueniPeeters, RalfZhou, ShuangCollins, PieterPhung, DTseng, VSWebb, GIHo, BGanji, MRashidi, L2019-07-272019-07-282019-07-272019-07-282018978-3-319-93034-3 -- 978-3-319-93033-60302-97431611-3349https://dx.doi.org/10.1007/978-3-319-93034-3_14https://hdl.handle.net/20.500.12418/649222nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) -- JUN 03-06, 2018 -- Deakin Univ, Melbourne, AUSTRALIAThis 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.en10.1007/978-3-319-93034-3_14info:eu-repo/semantics/closedAccessConference Object109371821692-s2.0-85049369993Q3WOS:000443224400014N/A