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Article

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Title

On feature extraction using distances from reference points

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Published in

Foundations of Computing and Decision Sciences

Journal year: 2024 | Journal volume: vol. 49 | Journal number: no. 3

Article type

scientific article

Publication language

english

Keywords
EN
  • Feature extraction
  • Classification
  • Reference points
Abstract

EN Feature extraction is the key to a successfully trained classifier. Al- though many automatic methods exist for traditional data, other data types (e.g., sequences, graphs) usually require dedicated approaches. In this paper, we study a universal feature extraction method based on distance from reference points. First, we formalize this process and provide an instantiation based on network centrality. To reliably select the best reference points, we introduce the notion of θ-neighborhood which allows us to navigate the topography of fully connected graphs. Our exper- iments show that the proposed peak selection method is significantly better than a traditional top-k approach for centrality-based reference points and that the quality of the reference points is much less important than their quantity. Finally, we provide an alternative, neural network interpretation of reference points, which paves a path to optimization-based selection methods, together with a new type of neuron, called the Euclidean neuron, and the necessary modifications to backpropagation.

Date of online publication

19.09.2024

Pages (from - to)

287 - 302

DOI

10.2478/fcds-2024-0015

URL

https://sciendo.com/pl/article/10.2478/fcds-2024-0015

License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

40

Impact Factor

1,8 [List 2023]

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