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Locality and Sparsity Preserving Embedding Convolutional Neural Network for Image Classification

  • Yu Xia
  • Yongzhao ZhanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11935)

Abstract

Convolutional neural networks (CNN) combined with manifold structures have attracted much attention, because they preserve local manifolds that are crucial and effective to reflect the intrinsic structure of data. In view of the excellent performance in image classification of CNN and the success in dimensionality reduction of manifold learning. This paper proposes a new deep learning framework based on deep CNN for image classification, which is called Locality and Sparsity Preserving Embedding Convolutional Neural Network (LSPE-CNN), that simultaneously considers the local information and natural sparsity of data into deep CNN. Compared to existing models, our proposed framework not only better preserve the associated features in the dataset, but also tries to seek the reconstruction relationship among samples by combining different manifold learning methods embedded in the CNN model, which further enhances the feature representation ability of the network. Experiments on CIFAR-10 and CIFAR-100 for image classification indicate that the proposed framework is superior to the other methods proposed in the deep learning literature.

Keywords

Convolutional neural networks Feature representation Manifold learning Image classification 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61672268) and the Primary Research & Development Plan of Jiangsu Province (No. BE2015137).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and Communication EngineeringJiangsu UniversityZhenjiangChina

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