Discovering the Dimension of Abstractness: Structure-Based Model that Learns New Categories and Categorizes on Different Levels of Abstraction

Authors: Georgi I. Petkov, Ivan I. Vankov, Yolina A. Petrova

Abstract:

A structure-based model of category learning and categorization at different levels of abstraction is presented. The model compares different structures and expresses their similarity implicitly in the forms of mappings. Based on this similarity, the model can categorize different targets either as members of categories that it already has or creates new categories. The model is novel using two threshold parameters to evaluate the structural correspondence. If the similarity between two structures exceeds the higher threshold, a new sub-ordinate category is created. Vice versa, if the similarity does not exceed the higher threshold but does the lower one, the model creates a new category on higher level of abstraction.

Keywords: Analogy-making, categorization, learning of categories, abstraction, hierarchical structure.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1317420

References:

[1] Collins, Allan M., and M. Ross Quillian. "Retrieval time from semantic memory." Journal of verbal learning and verbal behavior 8.2 (1969): 240-247.
[2] Collins, Allan M., and Elizabeth F. Loftus. "A spreading-activation theory of semantic processing." Psychological review 82.6 (1975): 407.
[3] Tenenbaum, Joshua B., et al. "How to grow a mind: Statistics, structure, and abstraction." Science 331.6022 (2011): 1279-1285.
[4] Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. Learning internal representations by error propagation. No. ICS-8506. California Univ San Diego La Jolla Inst for Cognitive Science, 1985.
[5] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
[6] Amos, Brandon, Bartosz Ludwiczuk, and Mahadev Satyanarayanan. "Openface: A general-purpose face recognition library with mobile applications." CMU School of Computer Science (2016).
[7] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. "Speech recognition with deep recurrent neural networks." Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 2013.
[8] Zhou, Bolei, Lapedriza, A., Xiao, J., Torralba, A., & Oliva, A. "Learning deep features for scene recognition using places database." Advances in neural information processing systems. 2014.
[9] Ramanathan, Vignesh, et al. "Learning semantic relationships for better action retrieval in images." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
[10] Kuehne, Sven, et al. "SEQL: Category learning as progressive abstraction using structure mapping." Proceedings of the 22nd annual meeting of the cognitive science society. Vol. 4. 2000.
[11] Falkenhainer, Brian, Kenneth D. Forbus, and Dedre Gentner. "The structure-mapping engine: Algorithm and examples." Artificial intelligence 41.1 (1989): 1-63
[12] Kokinov, B. "The context-sensitive cognitive architecture DUAL." Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. 1994.
[13] Petkov, Georgi, and Luiza Shahbazyan. "The RecMap model of active recognition based on analogical mapping." Proceedings of the 2007 International Conference on Cognitive Modeling. 2007.
[14] Biederman, Irving. "Recognition-by-components: a theory of human image understanding." Psychological review 94.2 (1987): 115.
[15] Hummel, John E. "Where view-based theories break down: The role of structure in shape perception and object recognition." Cognitive dynamics: Conceptual change in humans and machines (2000): 157-185.
[16] Hummel, John E., and Brian J. Stankiewicz. "Two roles for attention in shape perception: A structural description model of visual scrutiny." Visual Cognition 5.1-2 (1998): 49-79.
[17] Gentner, Dedre. "Structure-mapping: A theoretical framework for analogy." Cognitive science 7.2 (1983): 155-170.