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Discriminative stacked autoencoder for feature representation and classification

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References

  1. 1

    Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313: 504–507

  2. 2

    Gao Y P, Gao L, Li X Y, et al. A zero-shot learning method for fault diagnosis under unknown working loads. J Intell Manuf, 2019, 3: 1–11

  3. 3

    Mehta J, Majumdar A. RODEO: robust DE-aliasing autoencoder for real-time medical image reconstruction. Pattern Recogn, 2017, 63: 499–510

  4. 4

    Zhu Z T, Wang X G, Bai S, et al. Deep learning representation using autoencoder for 3D shape retrieval. Neurocomputing, 2016, 204: 41–50

  5. 5

    Fan Y J. Autoencoder node saliency: selecting relevant latent representations. Pattern Recogn, 2019, 88: 643–653

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant No. 51721092), Natural Science Foundation of Hubei Province (Grant No. 2018CFA078), and the Program for HUST Academic Frontier Youth Team (Grant No. 2017QYTD04).

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Correspondence to Liang Gao.

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Gao, Y., Li, X. & Gao, L. Discriminative stacked autoencoder for feature representation and classification. Sci. China Inf. Sci. 63, 120111 (2020). https://doi.org/10.1007/s11432-019-2722-3

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