short-paper

Transductive Domain Adaptation with Affinity Learning

Authors:

Longin Jan LateckiAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1903 - 1906

https://doi.org/10.1145/2806416.2806643

Published: 17 October 2015 Publication History

Abstract

We study the problem of domain adaptation, which aims to adapt the classifiers trained on a labeled source domain to an unlabeled target domain. We propose a novel method to solve domain adaptation task in a transductive setting. The proposed method bridges the distribution gap between source domain and target domain through affinity learning. It exploits the existence of a subset of data points in target domain which distribute similarly to the data points in the source domain. These data points act as the bridge that facilitates the data similarities propagation across domains. We also propose to control the relative importance of intra- and inter-domain similarities to boost the similarity propagation. In our approach, we first construct the similarity matrix which encodes both the intra- and inter-domain similarities. We then learn the true similarities among data points in joint manifold using graph diffusion.

We demonstrate that with improved similarities between source and target data, spectral embedding provides a better data representation, which boosts the prediction accuracy. The effectiveness of our method is validated on standard benchmark datasets for visual object recognition (multi-category).

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Cited By

Satya Rajendra Singh RSanodiya RArun P(2022)Joint Geometrical and Statistical Alignment Using Triplet Loss for Deep Domain AdaptationResponsible Data Science10.1007/978-981-19-4453-6_8(119-130)Online publication date: 15-Nov-2022
https://doi.org/10.1007/978-981-19-4453-6_8
Sanodiya RYao L(2020)A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain AdaptationSensors10.3390/s2016436720:16(4367)Online publication date: 5-Aug-2020
https://doi.org/10.3390/s20164367
Sanodiya RYao L(2020)Unsupervised Transfer Learning via Relative Distance ComparisonsIEEE Access10.1109/ACCESS.2020.30026668(110290-110305)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3002666
Show More Cited By

Index Terms

Transductive Domain Adaptation with Affinity Learning

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Published In

cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 October 2015

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National Science Foundation

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Satya Rajendra Singh RSanodiya RArun P(2022)Joint Geometrical and Statistical Alignment Using Triplet Loss for Deep Domain AdaptationResponsible Data Science10.1007/978-981-19-4453-6_8(119-130)Online publication date: 15-Nov-2022
https://doi.org/10.1007/978-981-19-4453-6_8
Sanodiya RYao L(2020)A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain AdaptationSensors10.3390/s2016436720:16(4367)Online publication date: 5-Aug-2020
https://doi.org/10.3390/s20164367
Sanodiya RYao L(2020)Unsupervised Transfer Learning via Relative Distance ComparisonsIEEE Access10.1109/ACCESS.2020.30026668(110290-110305)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3002666
Sanodiya RMathew JSaha STripathi P(2020)Particle swarm optimization based parameter selection technique for unsupervised discriminant analysis in transfer learning frameworkApplied Intelligence10.1007/s10489-020-01710-750:10(3071-3089)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s10489-020-01710-7
Sanodiya RTiwari MMathew JSaha SSaha S(2020)A particle swarm optimization-based feature selection for unsupervised transfer learningSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05105-124:24(18713-18731)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s00500-020-05105-1
Sanodiya RMathew JPaul BJose B(2019)A Kernelized Unified Framework for Domain AdaptationIEEE Access10.1109/ACCESS.2019.29587367(181381-181395)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2958736
Wang JLi XDu J(2019)Label Space Embedding of Manifold Alignment for Domain AdaptionNeural Processing Letters10.1007/s11063-018-9822-849:1(375-391)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s11063-018-9822-8
Sanodiya RSharma CMathew J(2019)Unified Framework for Visual Domain Adaptation Using Globality-Locality Preserving ProjectionsNeural Information Processing10.1007/978-3-030-36708-4_28(340-351)Online publication date: 12-Dec-2019
https://dl.acm.org/doi/10.1007/978-3-030-36708-4_28
Zang SCheng YWang XYu Q(2018)Semi-supervised transfer discriminant analysis based on cross-domain mean constraintArtificial Intelligence Review10.1007/s10462-016-9533-349:4(581-595)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10462-016-9533-3
Ionescu RButnaru A(2018)Transductive Learning with String Kernels for Cross-Domain Text ClassificationNeural Information Processing10.1007/978-3-030-04182-3_42(484-496)Online publication date: 18-Nov-2018
https://doi.org/10.1007/978-3-030-04182-3_42

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