Abstract
In this paper, a Hierarchical Pooling based Extreme Learning Machine (HPELM) is proposed for image classification. Extreme Learning Machine based on Local Receptive Fields (ELM-LRF) has been proved to be powerful for image classification. However, ELM-LRF is a shallow network and the features extracted by ELM-LRF is low-level. To obtain better results, one need to enlarge the dimension of the hidden features. This paper extends the concept of deep learning to ELM-LRF. Random convolutional nodes and hierarchical pooling structures are constructed for capturing high level semantic features. HPELM has the ability of feature extraction and classification. It improves the classification performance of ELM-LRF without increasing the number of the neuron in the last hidden layer. Experiments on the MNIST and NORB datasets demonstrate the attractive performance of HPELM even compared with the state-of-the-art algorithms.
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Liu, Y., Liu, Z., Lei, Z. (2020). Hierarchical Pooling Based Extreme Learning Machine for Image Classification. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_1
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