Preprint Article Version 1 This version is not peer-reviewed

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

Version 1 : Received: 29 January 2020 / Approved: 31 January 2020 / Online: 31 January 2020 (04:38:51 CET)

A peer-reviewed article of this Preprint also exists.

Sharma, R.; Ribeiro, B.; Miguel Pinto, A.M.P.A.A.; Cardoso, F.A. Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts. Appl. Sci. 2020, 10, 1994. Sharma, R.; Ribeiro, B.; Miguel Pinto, A.M.P.A.A.; Cardoso, F.A. Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts. Appl. Sci. 2020, 10, 1994.

Journal reference: Appl. Sci. 2020, 10, 1994
DOI: 10.3390/app10061994

Abstract

The term Concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. The Concepts are also studied computationally through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of Abstract Concepts by exploiting the geometrical view of Concepts, without supervision. In the article, the IRIS data was used to demonstrate: the RAN's modeling; flexibility in concept identifier choice; and deep hierarchy generation. Data from IoT's Human Activity Recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with 8 UCI benchmarks and the comparisons with 5 Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RAN's hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.

Subject Areas

unsupervised machine learning; hierarchical learning; computational representation; computational cognitive modeling; contextual modeling; classification; IoT data modeling

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