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A-optimal convolutional neural network

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Abstract

In this paper, we propose a novel data representation-classification model learning algorithm. The model is a convolutional neural network (CNN), and we learn its parameters to achieve A-optimality. The input multi-instance data are represented by a CNN model, and then classified by a linear classification model. The A-optimality of a classification model is measured by the trace of the covariance matrix of the model parameter vector. To achieve the A-optimality of the CNN model, we minimize the classification errors and a regularization term to present the classification model parameter as a function of the CNN filter parameters, and minimize its trace of the covariance matrix. We show that the minimization problem can be solved easily by transferring it to another coupled minimization problem. In the experiments over benchmark data sets of molecular, image, and seismic waveform, we show the advantages of the proposed A-optimal CNN model.

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Acknowledgements

This work was supported by China Scholarship Council (201507005063) and Key Laboratory of High-speed Railway Engineering (Southwest Jiaotong University), Ministry of Education.

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Authors and Affiliations

  1. School of Civil Engineering, Southwest Jiao Tong University, Chengdu, 610031, China

    Zihong Yin, Dehui Kong & Guoxia Shao

  2. School of Information Science and Technology, Southwest Jiao Tong University, Chengdu, 610031, China

    Xinran Ning

  3. The Commonwealth Scientific and Industrial Research Organisation, GPO Box 664, Canberra, ACT, 2601, Australia

    Warren Jin

  4. New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

    Jing-Yan Wang

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  1. Zihong Yin
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  2. Dehui Kong
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  3. Guoxia Shao
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  5. Warren Jin
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  6. Jing-Yan Wang
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Correspondence to Zihong Yin.

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Yin, Z., Kong, D., Shao, G. et al. A-optimal convolutional neural network. Neural Comput & Applic 30, 2295–2304 (2018). https://doi.org/10.1007/s00521-016-2783-9

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