Conference Proceedings


Cathal Gurrin
TengQi Ye
Tianchun Wang


Computer Science

brain image representation semi supervised learning human machine learning transfer state of the art transfer learning

Transfer nonnegative matrix factorization for image representation (2016)

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
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Cathal Gurrin, TengQi Ye, Tianchun Wang

Experts in our system

Cathal Gurrin
Dublin City University
Total Publications: 206
TengQi Ye
Dublin City University
Total Publications: 4