A mapping and principal component dimensionality reduction analysis neural network for structured multi-classification

Xuxi Ye; Haodong Tian; Jiapeng Yang; Yixin Luo; Fuli Liu

doi:10.1117/12.3003995

11 October 2023 A mapping and principal component dimensionality reduction analysis neural network for structured multi-classification

Xuxi Ye, Haodong Tian, Jiapeng Yang, Yixin Luo, Fuli Liu

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128002E (2023) https://doi.org/10.1117/12.3003995
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Structured multi-classification is a typical supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. This problem is often plagued by the slow training speed and the low classification accuracy. The goal of this paper is to propose a novel framework called normalizing quadratic Mapping and principal component Dimensionality Reduction analysis (MDR) Neural Network. Thorough experiments demonstrate it sets the new state of the art, accelerating the convergence of neural network and improving the accuracy of classification recognition with a few number of training rounds.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuxi Ye, Haodong Tian, Jiapeng Yang, Yixin Luo, and Fuli Liu "A mapping and principal component dimensionality reduction analysis neural network for structured multi-classification", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128002E (11 October 2023); https://doi.org/10.1117/12.3003995

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