Fast feedforward non-parametric deep learning network with automatic feature extraction

Plamen Parvanov Angelov, Xiaowei Gu, Jose Principe

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

3 Citations (SciVal)

Abstract

In this paper, a new type of feedforward non-parametric deep learning network with automatic feature extraction is proposed. The proposed network is based on human-understandable local aggregations extracted directly from the images. There is no need for any feature selection and parameter tuning. The proposed network involves nonlinear transformation, segmentation operations to select the most distinctive features from the training images and builds RBF neurons based on them to perform classification with no weights to train. The design of the proposed network is very efficient (computation and time wise) and produces highly accurate classification results. Moreover, the training process is parallelizable, and the time consumption can be further reduced with more processors involved. Numerical examples demonstrate the high performance and very short training process of the proposed network for different applications.
Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN), 14/05/17
Subtitle of host publicationProceedings
PublisherIEEE Press
Pages534-541
Number of pages8
ISBN (Electronic)9781509061815
DOIs
Publication statusE-pub ahead of print - 03 Jul 2017
Externally publishedYes

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Keywords

  • Deep learning
  • Fast training
  • Feature extraction
  • Feedforward
  • Learning network

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