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A novel multi-scale and sparsity auto-encoder for classification

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Abstract

The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and L21-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the L21-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods.

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Acknowledgements

This work is partly supported by the National Natural Science Foundation of China (Projects numbers: 61673194, 61672263, 61672265, 62076110, 61673193), the Natural Science Foundation of Jiangsu Province (Project number: BK20181341) and the national first-class discipline program of Light Industry Technology and Engineering (Project number: LITE2018-25).

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Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi, 214122, China

    Huiling Wang

  2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China

    Jun Sun, Xiaofeng Gu & Wei Song

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  1. Huiling Wang
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Correspondence to Jun Sun.

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Wang, H., Sun, J., Gu, X. et al. A novel multi-scale and sparsity auto-encoder for classification. Int. J. Mach. Learn. & Cyber. 13, 3909–3925 (2022). https://doi.org/10.1007/s13042-022-01632-5

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