4.8 Article

C2DNDA: A Deep Framework for Nonlinear Dimensionality Reduction

Journal

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 68, Issue 2, Pages 1684-1694

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2020.2969072

Keywords

Classification; convolutional neural networks (CNNs); dimensionality reduction; two-dimensional linear discriminant analysis (2DLDA)

Funding

  1. National Key R&D Program of China [2018YFB1107403]
  2. National Natural Science Foundation of China [U1864204, 61773316, U1801262, 61871470, 61761130079]
  3. Project of Special Zone for National Defense Science and Technology Innovation

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A novel convolutional two-dimensional nonlinear discriminant analysis method is proposed for dimensionality reduction, utilizing the nonlinearity of convolutional neural networks (CNNs) and learning ability. Experimental results in various image-related applications demonstrate the effectiveness of the method.
Dimensionality reduction has attracted much research interest in the past few decades. Existing dimensionality reduction methods like linear discriminant analysis and principal component analysis have achieved promising performance, but the single and linear projection properties limit further improvements of performance. A novel convolutional two-dimensional nonlinear discriminant analysis method is proposed for dimensionality reduction in this article. In order to handle nonlinear data properly, we present a newly designed structure with convolutional neural networks (CNNs) to realize an equivalent objective function with classical two-dimensional linear discriminant analysis (2DLDA) and thus embed the original 2DLDA into an end-to-end network. In this way, the proposed dimensionality reduction network can utilize the nonlinearity of the CNN and benefit from the learning ability. The results of experiment on different image-related applications demonstrate that our method outperforms other comparable approaches, and its effectiveness is proved.

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