Neural network representations for the inter- and intra-class common vector classifiers

EDİZKAN R., Barkana A., Koc M., GÜLMEZOĞLU M. B., Ashames M. M., ERGİN S., ...More

Digital Signal Processing: A Review Journal, vol.142, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 142
  • Publication Date: 2023
  • Doi Number: 10.1016/j.dsp.2023.104205
  • Journal Name: Digital Signal Processing: A Review Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC
  • Keywords: Artificial neural networks, Discriminant common vectors, Linear regression, Subspace classifiers
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

Common Vector Approach (CVA) is a known linear regression-based classifier, which also enables an extension to inter-class discrimination, known as the Discriminative Common Vector Approach (DCVA). The characteristics of linear regression classifiers (LRCs) enable the possibility of a schematic implementation that is similar to the neuron model of artificial neural networks (ANNs). In this work, we explore this schematic similarity to come up with an ANN representation for both CVA and DCVA. The new representation eliminates the need for projection matrices in its implementation, hence significantly reduces the memory requirements and computational complexities of the processes. Furthermore, since the new representation is in a neural style, it is expected to provide a solid and intriguing extension of CVA (and DCVA) by further incorporating adaptation or activation processes to the already successful CVA-based classifiers.