Heterogeneous Domain Adaptation Based on Class Decomposition Schemes

Firat Ismailoglu, Evgueni Smirnov, Ralf Peeters, Shuang Zhou, Pieter Collins

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publicationPAKDD 2018
EditorsD. Phung, V. Tseng, G. Webb, B. Ho, M. Ganji, L. Rashidi
Place of PublicationCham
PublisherSpringer
Pages169-182
ISBN (Electronic)978-3-319-93034-3
ISBN (Print)978-3-319-93033-6
DOIs
Publication statusPublished - 19 Jun 2018

Publication series

SeriesLecture Notes in Computer Science
Volume10937

Cite this

Ismailoglu, F., Smirnov, E., Peeters, R., Zhou, S., & Collins, P. (2018). Heterogeneous Domain Adaptation Based on Class Decomposition Schemes. In D. Phung, V. Tseng, G. Webb, B. Ho, M. Ganji, & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining: PAKDD 2018 (pp. 169-182). Springer. Lecture Notes in Computer Science, Vol.. 10937 https://doi.org/10.1007/978-3-319-93034-3_14