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Cognitive Computation
Erschienen in: Cognitive Computation 3/2014

01.09.2014

Modular Composite Representation

verfasst von: Javier Snaider, Stan Franklin

Erschienen in: Cognitive Computation | Ausgabe 3/2014

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Abstract

High-dimensional vector spaces have noteworthy properties that make them attractive for representation models. A reduced description model is a mechanism for encoding complex structures as single high-dimensional vectors. Moreover, these vectors can be used to directly process complex operations such as analogies, inferences, and structural comparisons. Also, it is possible to reconstruct the whole structure from the reduced description vector. Here, we introduce the modular composite representation (MCR), a new reduced description model that employs long integer vectors. We also describe several experiments with them, and give a theoretical analysis of the distance distribution in this vector space, and of properties of this representation. Finally, we compare MCR with other two reduced description models: Spatter Code and holographic reduced representation.
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Fußnoten
1
In the context of this work, we define elements as things that can be represented, for example, objects, actions, features, events, etc.
 
2
Some systems can create reduced descriptions without explicitly defining these operations. For example see RAAM [25].
 
3
Actually, XOR is a special case of the modular sum when r = 2.
 
4
This vector could have been omitted, but we chose to follow Plate’s example that included it.
 
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Metadaten
Titel
Modular Composite Representation
verfasst von
Javier Snaider
Stan Franklin
Publikationsdatum
01.09.2014
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 3/2014
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-013-9243-y

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