A Generalized Kernel Approach to Dissimilarity-based Classification

By Elzbieta Pekalska, Pavel Paclík, Robert P. W. Duin. Journal of machine learning research, 2002

423 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/249297cebeede0304f2d88e7dee04fe1979bbada

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Citations in the corpus

Secure Kernel Machines against Evasion Attacks (2016, methodology): “This is a well-known technique in similarity-based classification, which amounts to learning the SVM on the squared kernel [10, 27].”
Learning with Distance Substitution Kernels (2004, methodology): “Statements on theoretical properties of the kernels follow in Section 3 and comments on consequences for use in SVMs are given in Section 4.”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, background): “triangle inequality: d ( x, y )< d(x, z)+d(z, y) for every z While reflectivity and positivity are crucial to define a proper dissimilarity measure d (·, ·), the latter two properties are actually not essential [105], which is important given that nonmetric distances often seem to arise e.”
Prototype selection for dissimilarity-based classifiers (2006, methodology): “For this reason, even a randomly selected representation set may work well [5].”
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions (2002, background): “The attractiveness of considering objects embedded in a derived (dis)similarity space, as opposed to the original feature space, has been recently shown in several classification applications (Pekalska et al., 2002, Strehl and Ghosh, 2002b).”
Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces (2006, background): “linear and quadratic normal density based classifiers, tend to perform well in dissimilarity spaces [19,18,20,21,31].”
Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery (2005, methodology): “In [21], a kernel-based clustering algorithm was proposed and in [17] kernels were used as generalized dissimilarity measures for classification.”
Similarity-based Classification: Concepts and Algorithms (2009, methodology): “In addition, Pekalska et al. consider generative classifiers for similarity feature vectors; they propose a regularized Fisher linear discriminant classifier [26] and a regularized quadratic discriminant classifier [30] In the formulation above, we attempt to learn n + 1 parameters using n training ”
Kernel Methods for Relation Extraction (2002, methodology): “Incidentally, SVM can be adapted for the pseudo-euclidean representations (Graepel et al., 1999, Pekalska et al., 2001), hence, lending the power of regularization to learning in structural domains, where natural distance functions exist.”
Grassmann discriminant analysis: a unifying view on subspace-based learning (2008, methodology): “There have been e.orts to use inde.nite kernels for learning (Ong et al., 2004; Haasdonk, 2005), and several heuristics have been proposed to make an inde.nite kernel matrix to a positive de.nite matrix (Pekalska et al., 2002).”
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition (2006, background): “A number of solutions have been suggested for this issue [29].”
Optimizing the kernel in the empirical feature space (2005, unknown): “Section V contains the conclusion.”