Representation learning based on hybrid polynomial approximated extreme learning machine

As an effective algorithm in feature learning, autoencoder (AE) and its variants have been widely applied in machine learning. To overcome the expensive time consumption in backpropagation learning and parameters iterative tuning, extreme learning machine (ELM) is combined with AE, developed as ELM-based AE (ELM-AE) in unsupervised feature learning. However, random projection of ELM makes the learned features not stable for the final target recognition. On the other hand, considering to enhance high-order nonlinear expression in ELM-AE, common methods increase the computation overhead. Therefore, this paper proposes to construct a new ELM-AE based on approximation of hybrid high-order polynomial functions. The proposed model will keep fast learning speed by linearization of the high-order nonlinear expression, be robust to random projection issue, and learn discriminative features for pattern recognition. Two feature learning application scenarios, feature reconstruction and dimension reduction, are discussed based on different ELM-AE models. Experiments on publicly available datasets including small and large datasets demonstrate the proposed model's feasibility and superiority in feature learning.

Automated summary

The paper introduces a new ELM-AE model based on approximation of hybrid high-order polynomial functions to address issues in existing models and improve the effectiveness of feature learning.