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Analogy retrieval and processing with distributed vector representations
Author(s) -
Plate Tony A.
Publication year - 2000
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/1468-0394.00125
Subject(s) - computer science , similarity (geometry) , analogy , encode , similitude , dot product , encoding (memory) , artificial intelligence , euclidean vector , pattern recognition (psychology) , algorithm , mathematics , philosophy , linguistics , biochemistry , chemistry , geometry , image (mathematics) , gene
Holographic reduced representations (HRRs) are a method for encoding nested relational structures in fixed‐width vector representations. HRRs encode relational structures as vector representations in such a way that the superficial similarity of the vectors reflects both superficial and structural similarity of the relational structures. HRRs also support a number of operations that could be very useful in psychological models of human analogy processing: fast estimation of superficial and structural similarity via a vector dot‐product; finding corresponding objects in two structures; and chunking of vector representations. Although similarity assessment and discovery of corresponding objects both theoretically take exponential time to perform fully and accurately, with HRRs one can obtain approximate solutions in constant time. The accuracy of these operations with HRRs mirrors patterns of human performance on analog retrieval and processing tasks.