Heterogeneous Domain Adaptation Based on Class Decomposition Schemes

İSMAİLOĞLU F., Smirnov E., Peeters R., Zhou S., Collins P.

22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Melbourne, Avustralya, 3 - 06 Haziran 2018, cilt.10937, ss.169-182 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 10937
  • Doi Numarası: 10.1007/978-3-319-93034-3_14
  • Basıldığı Şehir: Melbourne
  • Basıldığı Ülke: Avustralya
  • Sayfa Sayıları: ss.169-182
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

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.