The Dissimilarity Representation for Pattern Recognition - Foundations and Applications

By Elzbieta Pekalska, Robert P. W. Duin. Series in Machine Perception and Artificial Intelligence, 2005

676 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6c425df743d37fda090a43f01da233fb5e0ba300

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In Artificial Intelligence

Citations in the corpus

Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces (2006, methodology): “The reason is that such dissimilarities will be approximately normally distributed thanks to the central limit theorem (if one or few variances are dominant, then they will approximate the 2 distribution) [17].”
Hierarchical graph embedding in vector space by graph pyramid (2017, background): “The first group is inspired by dissimilarity representations proposed in [12].”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, methodology): “Implicit to the dissimilarity paradigm is the idea that the notion of proximity (similarity or dissimilarity) is more fundamental than the notion of feature or of class [104].”
Graph Embedding in Vector Spaces by Means of Prototype Selection (2007, methodology): “Originally, this idea was proposed in order to map patterns into dissimilarity spaces [2, 3 ].”
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, background): “Finally, recent works suggest that changing the representation of the problem, i.e. taking into account the pairwise differences among the data (Pekalska & Duin, 2005) may somehow overcome the issue of small disjuncts (Garćıa, Sánchez, de J. Ochoa Domı́nguez, & Cleofas-Sánchez, 2015).”
Prototype-based Neural Network Layers: Incorporating Vector Quantization (2018, background): “For a detailed consideration we refer to [10, 11].”