1 research outputs found
Feature Graph Architectures
In this article we propose feature graph architectures (FGA), which are deep
learning systems employing a structured initialisation and training method
based on a feature graph which facilitates improved generalisation performance
compared with a standard shallow architecture. The goal is to explore
alternative perspectives on the problem of deep network training. We evaluate
FGA performance for deep SVMs on some experimental datasets, and show how
generalisation and stability results may be derived for these models. We
describe the effect of permutations on the model accuracy, and give a criterion
for the optimal permutation in terms of feature correlations. The experimental
results show that the algorithm produces robust and significant test set
improvements over a standard shallow SVM training method for a range of
datasets. These gains are achieved with a moderate increase in time complexity.Comment: 9 pages, with 5 pages of supplementary material (appendices