Elmnet: Feature learning using extreme learning machines
Dongshun Cui, Guang-Bin Huang, L.L. Chamara Kasun, Guanghao Zhang, Wei Han · 2017
Feature learning is an initial step applied to computer vision tasks and is broadly categorized as: 1) deep feature learning; 2) shallow feature learning. In this paper we focus on shallow feature learning as these algorithms require less computational resources than deep feature learning algorithms. In this paper we propose a shallow feature learning algorithm referred to as Extreme Learning Machine Network (ELMNet). ELMNet is module based neural network consist of feature learning module and a post-processing module. Each feature learning module in ELMNet performs the following operations: 1) patch-based mean removal; 2) ELM auto-encoder (ELM-AE) to learn features. Post-processing module is inserted after the feature learning module and simplifies the features learn by the feature learning modules by hashing and block-wise histogram. Proposed ELMNet outperforms shallow feature learning algorithm PCANet on the MNIST handwritten dataset.