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Transfer nonnegative matrix factorization for image representation

Wang, Tianchun, Ye, TengQi and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2016) Transfer nonnegative matrix factorization for image representation. In: The 22nd International Conference on Multimedia Modelling (MMM'16), 4-6 Jan 2016, Miami, FL.. ISBN 978-3-319-27673-1

Abstract
Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts based in the human brain. However, when labeled and unlabeled images are sampled from different distributions, they may be quantized into different basis vector space and represented in different coding vector space, which may lead to low representation fidelity. In this paper, we investigate how to extend NMF to cross-domain scenario. We accomplish this goal through TNMF - a novel semi-supervised transfer learning approach. Specifically, we aim to minimize the distribution divergence between labeled and unlabeled images, and incorporate this criterion into the objective function of NMF to construct new robust representations. Experiments show that TNMF outperforms state-of-the-art methods on real datasets
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of MMM 2016 - The 22nd International Conference on Multimedia Modeling. Lecture Notes in Computer Science 9517. Springer. ISBN 978-3-319-27673-1
Publisher:Springer
Copyright Information:(c) 2016 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21031
Deposited On:15 Jan 2016 15:39 by Tengqi Ye . Last Modified 15 Dec 2021 16:15
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