Next Article in Journal
Unsupervised Generation and Synthesis of Facial Images via an Auto-Encoder-Based Deep Generative Adversarial Network
Next Article in Special Issue
Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge
Previous Article in Journal
Mercury in Juvenile Solea senegalensis: Linking Bioaccumulation, Seafood Safety, and Neuro-Oxidative Responses under Climate Change-Related Stressors
Previous Article in Special Issue
A New Face Recognition Method for Intelligent Security
Article

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

Department of Informatics Engineering—University of Coimbra, 3030-290 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(6), 1994; https://doi.org/10.3390/app10061994
Received: 29 January 2020 / Revised: 5 March 2020 / Accepted: 10 March 2020 / Published: 14 March 2020
The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain 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, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with five Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs 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. View Full-Text
Keywords: unsupervised machine learning; hierarchical learning; computational representation; computational cognitive modeling; contextual modeling; classification; IoT data modeling unsupervised machine learning; hierarchical learning; computational representation; computational cognitive modeling; contextual modeling; classification; IoT data modeling
Show Figures

Figure 1

MDPI and ACS Style

Sharma, R.; Ribeiro, B.; Miguel Pinto, A.; Cardoso, F.A. Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts. Appl. Sci. 2020, 10, 1994. https://doi.org/10.3390/app10061994

AMA Style

Sharma R, Ribeiro B, Miguel Pinto A, Cardoso FA. Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts. Applied Sciences. 2020; 10(6):1994. https://doi.org/10.3390/app10061994

Chicago/Turabian Style

Sharma, Rahul, Bernardete Ribeiro, Alexandre Miguel Pinto, and F. A. Cardoso. 2020. "Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts" Applied Sciences 10, no. 6: 1994. https://doi.org/10.3390/app10061994

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop