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Learning distance function by coding similarity
Aharon Bar Hillel
*
,
Daphna Weinshall
*
Corresponding author for this work
The Rachel and Selim Benin School of Engineering and Computer Science
Research output
:
Contribution to conference
›
Paper
›
peer-review
13
Scopus citations
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Keyphrases
Distance Function
100%
Similarity Function
100%
Mahalanobis Distance
66%
Information Theory
33%
Image Retrieval
33%
Likelihood Ratio Test
33%
Superior Performance
33%
Joint Encoding
33%
Graph Clustering
33%
Number of Data
33%
Mahalanobis
33%
Fisher Linear Discriminant
33%
Similarity Preserving
33%
Point Pairs
33%
Gaussian Assumption
33%
Sampling Conditions
33%
Retrieval-based
33%
Image Graph
33%
Computer Science
Similarity Function
100%
Distance Function
100%
Equivalence Constraint
66%
Mahalanobis Distance
66%
Image Retrieval
33%
Linear Discriminant
33%
Likelihood Ratio
33%
Superior Performance
33%
Gaussian Assumption
33%
Joint Encoding
33%
Mathematics
Distance Function
100%
Function Similarity
100%
Mahalanobis Distance
66%
Gaussian Distribution
33%
Likelihood Ratio Test
33%
Superiority
33%
Theoretic Term
33%