Multi-Grained Cascade AdaBoost Extreme Learning Machine for Feature Representation

Extreme learning machine (ELM) has been well recognized for characteristics such as less training parameters, fast training speed and strong generalization ability. Due to its high efficiency, researchers have embedded ELMs into deep learning frameworks to address the problems of high time-consumption and computational complexities that are encountered in the traditional deep neural networks. However, existing ELM-based deep learning algorithms usually neglect the spatial relationship of original data. In this paper, we propose a multi-grained cascade AdaBoost based weighted ELM algorithm (gcAWELM) for feature representation. We use AdaBoost based weighted ELM as a basic module to construct cascade structure for feature learning. Different ensemble ELMs trend to extract varied features. Moreover, multi-grained scanning is employed to exploit the spatial structure of the original data. The gcAWELM can determine the number of cascade levels adaptively and has simpler structure and fewer parameters compared with the traditional deep models. The results on image datasets with different scales show that the gcAWELM can achieve competitive performance for different learning tasks even with the same parameter settings.