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., ...Daha Fazla

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

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 142
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.dsp.2023.104205
  • Dergi Adı: Digital Signal Processing: A Review Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: Artificial neural networks, Discriminant common vectors, Linear regression, Subspace classifiers
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

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.