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

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

Published
Publication date14/05/2017
Number of pages8
Pages534-541
Original languageEnglish
EventInternational Joint Conference on Neural Networks (IJCNN) -
Duration: 14/05/201719/05/2017

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN)
Period14/05/1719/05/17

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.