Correlation Projection for Analytic Learning of a Classification Network

Huiping Zhuang, Zhiping Lin, Kar Ann Toh

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this paper, we propose a correlation projection network (CPNet) that determines its parameters analytically for pattern classification. This network consists of multiple modules with each module containing two layers. We first introduce a label encoding process for each module to facilitate a locally supervised learning. Subsequently, in each module, the first layer conducts what we call the correlation projection process for feature extraction. The second layer determines its parameters analytically through solving a least squares problem. By introducing a corresponding label decoding process, the proposed CPNet achieves a multi-exit structure which is the first of its kind in multilayer analytic learning. Due to the analytic learning technique, the proposed method only needs to visit the dataset once, and is hence significantly faster than the commonly used backpropagation, as verified in the experiments. We also conduct classification tasks on various benchmark datasets which demonstrate competitive results compared with several state-of-the-arts.

Original languageEnglish
Pages (from-to)3893-3914
Number of pages22
JournalNeural Processing Letters
Issue number6
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
This work was supported in part by the Science and Engineering Research Council, Agency of Science, Technology and Research, Singapore, through the National Robotics Program under Grant No. 1922500054. The computational work for this article was partially performed on resources of the National Supercomputing Centre, Singapore ( ). This research was also partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant number: NRF-2018R1D1A1A09081956).

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Neuroscience(all)
  • Computer Networks and Communications
  • Artificial Intelligence


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