This paper proposes a kernel projection (KP) neural network that analytically determines its network parameters. The proposed network is composed of cascaded modules of 2-layer sub-networks. A technique which encodes the label information into each module has been introduced to enable a locally supervised learning. Such a supervised learning in the 2-layer module begins with a kernel projection in the first layer and determines its parameters analytically via solving a least squares problem in the second layer. We show that the analytic nature of the proposed network allows a learning process significantly faster than that of the traditional backpropagation method as it only needs to visit the dataset once. Experiments of classification tasks on various datasets are carried out, showing comparable or better results compared with several competing methods.
|Title of host publication||2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2021|
|Event||53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Daegu, Korea, Republic of|
Duration: 2021 May 22 → 2021 May 28
|Name||Proceedings - IEEE International Symposium on Circuits and Systems|
|Conference||53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021|
|Country/Territory||Korea, Republic of|
|Period||21/5/22 → 21/5/28|
Bibliographical noteFunding Information:
This work was partially supported by the Science and Engineering Research Council, Agency of Science, Technology and Research, Singapore, through the National Robotics Program under Grant No. 1922500052.
© 2021 IEEE
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering