Discriminative stacked autoencoder for feature representation and classification

This is a preview of subscription content, access via your institution.


  1. 1

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

    MathSciNet  Article  Google Scholar 

  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

    Google Scholar 

  3. 3

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  5. 5

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

    Article  Google Scholar 

Download references


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).

Author information



Corresponding author

Correspondence to Liang Gao.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation