Heterogeneous Domain Adaptation Based on Class Decomposition Schemes

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). Cham: Springer. Lecture Notes in Computer Science, Vol.. 10937 https://doi.org/10.1007/978-3-319-93034-3_14
Ismailoglu, Firat ; Smirnov, Evgueni ; Peeters, Ralf ; Zhou, Shuang ; Collins, Pieter. / Heterogeneous Domain Adaptation Based on Class Decomposition Schemes. Advances in Knowledge Discovery and Data Mining: PAKDD 2018. editor / D. Phung ; V. Tseng ; G. Webb ; B. Ho ; M. Ganji ; L. Rashidi. Cham : Springer, 2018. pp. 169-182 (Lecture Notes in Computer Science, Vol. 10937).
@inproceedings{a712d918418741189bc977d527e44f25,
title = "Heterogeneous Domain Adaptation Based on Class Decomposition Schemes",
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.",
author = "Firat Ismailoglu and Evgueni Smirnov and Ralf Peeters and Shuang Zhou and Pieter Collins",
year = "2018",
month = "6",
day = "19",
doi = "10.1007/978-3-319-93034-3_14",
language = "English",
isbn = "978-3-319-93033-6",
series = "Lecture Notes in Computer Science",
pages = "169--182",
editor = "D. Phung and V. Tseng and G. Webb and B. Ho and M. Ganji and L. Rashidi",
booktitle = "Advances in Knowledge Discovery and Data Mining",
publisher = "Springer",
address = "United States",

}

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. Springer, Cham, Lecture Notes in Computer Science, vol. 10937, pp. 169-182. https://doi.org/10.1007/978-3-319-93034-3_14

Heterogeneous Domain Adaptation Based on Class Decomposition Schemes. / Ismailoglu, Firat; Smirnov, Evgueni; Peeters, Ralf; Zhou, Shuang; Collins, Pieter.

Advances in Knowledge Discovery and Data Mining: PAKDD 2018. ed. / D. Phung; V. Tseng; G. Webb; B. Ho; M. Ganji; L. Rashidi. Cham : Springer, 2018. p. 169-182 (Lecture Notes in Computer Science, Vol. 10937).

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

TY - GEN

T1 - Heterogeneous Domain Adaptation Based on Class Decomposition Schemes

AU - Ismailoglu, Firat

AU - Smirnov, Evgueni

AU - Peeters, Ralf

AU - Zhou, Shuang

AU - Collins, Pieter

PY - 2018/6/19

Y1 - 2018/6/19

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-93034-3_14

DO - 10.1007/978-3-319-93034-3_14

M3 - Conference article in proceeding

SN - 978-3-319-93033-6

T3 - Lecture Notes in Computer Science

SP - 169

EP - 182

BT - Advances in Knowledge Discovery and Data Mining

A2 - Phung, D.

A2 - Tseng, V.

A2 - Webb, G.

A2 - Ho, B.

A2 - Ganji, M.

A2 - Rashidi, L.

PB - Springer

CY - Cham

ER -

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