Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Research Collection School Of Computing and Information Systems
Artificial intelligence; Mapping; Matrix algebra; Network architecture; Stochastic systems
Articles 1 - 1 of 1
Full-Text Articles in Physical Sciences and Mathematics
Building Deep Networks On Grassmann Manifolds, Zhiwu Huang, J. Wu, Gool L. Van
Building Deep Networks On Grassmann Manifolds, Zhiwu Huang, J. Wu, Gool L. Van
Research Collection School Of Computing and Information Systems
Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper proposes a deep network architecture by generalizing the Euclidean network paradigm to Grassmann manifolds. In particular, we design full rank mapping layers to transform input Grassmannian data to more desirable ones, exploit re-orthonormalization layers to normalize the resulting matrices, study projection pooling layers to reduce the model complexity in the Grassmannian context, and devise projection mapping layers to respect Grassmannian geometry and meanwhile achieve Euclidean forms for regular output layers. To train the Grassmann networks, …