Conference Proceedings


Cathal Gurrin
Yu Guo
Kevin McGuinness
Tianchun Wang
TengQi Ye


Computer Science

representation learning machine learning denoising autoencoder algorithms views multimedia fusion autoencoder multi view learning

Learning multiple views with orthogonal denoising autoencoders (2016)

Abstract Multi-view learning techniques are necessary when data is described by multiple distinct feature sets because single-view learning algorithms tend to overt on these high-dimensional data. Prior successful approaches followed either consensus or complementary principles. Recent work has focused on learning both the shared and private latent spaces of views in order to take advantage of both principles. However, these methods can not ensure that the latent spaces are strictly independent through encouraging the orthogonality in their objective functions. Also little work has explored representation learning techniques for multiview learning. In this paper, we use the denoising autoencoder to learn shared and private latent spaces, with orthogonal constraints | disconnecting every private latent space from the remaining views. Instead of computationally expensive optimization, we adapt the backpropagation algorithm to train our model.
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, Yu Guo, Kevin McGuinness, Tianchun Wang, TengQi Ye

Experts in our system

Cathal Gurrin
Dublin City University
Total Publications: 238
Kevin McGuinness
Dublin City University
Total Publications: 93
TengQi Ye
Dublin City University
Total Publications: 4