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| Content Provider | Springer Nature Link |
|---|---|
| Author | Snaider, Javier Franklin, Stan |
| Copyright Year | 2014 |
| 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. |
| Starting Page | 510 |
| Ending Page | 527 |
| Page Count | 18 |
| File Format | |
| ISSN | 18669956 |
| Journal | Cognitive Computation |
| Volume Number | 6 |
| Issue Number | 3 |
| e-ISSN | 18669964 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2014-01-23 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Reduced description Holistic record High-dimensional representation Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition |
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