In many real-world applications concerning pattern recognition techniques, it is of utmost importance the automatic learning of the most appropriate dissimilarity measure to be used in object comparison. Real-world objects are often complex entities and need a specific representation grounded on a composition of different heterogeneous features, leading to a non-metric starting space where Machine Learning algorithms operate. However, in the so-called unconventional spaces a family of dissimilarity measures can be still exploited, that is, the set of component-wise dissimilarity measures, in which each component is treated with a specific sub-dissimilarity that depends on the nature of the data at hand. These dissimilarities are likely to be non-Euclidean, hence the underlying dissimilarity matrix is not isometrically embeddable in a standard Euclidean space because it may not be structurally rich enough. On the other hand, in many metric learning problems, a component-wise dissimilarity measure can be defined as a weighted linear convex combination and weights can be suitably learned. This article, after introducing some hints on the relation between distances and the metric learning paradigm, provides a discussion along with some experiments on how weights, intended as mathematical operators, interact with the Euclidean behavior of dissimilarity matrices.

On component-wise dissimilarity measures and metric properties in pattern recognition / De Santis, Enrico; Martino, Alessio; Rizzi, Antonello. - In: PEERJ. COMPUTER SCIENCE.. - ISSN 2376-5992. - 8:(2022), pp. 1-26. [10.7717/peerj-cs.1106]

On component-wise dissimilarity measures and metric properties in pattern recognition

De Santis, Enrico
;
Rizzi, Antonello
2022

Abstract

In many real-world applications concerning pattern recognition techniques, it is of utmost importance the automatic learning of the most appropriate dissimilarity measure to be used in object comparison. Real-world objects are often complex entities and need a specific representation grounded on a composition of different heterogeneous features, leading to a non-metric starting space where Machine Learning algorithms operate. However, in the so-called unconventional spaces a family of dissimilarity measures can be still exploited, that is, the set of component-wise dissimilarity measures, in which each component is treated with a specific sub-dissimilarity that depends on the nature of the data at hand. These dissimilarities are likely to be non-Euclidean, hence the underlying dissimilarity matrix is not isometrically embeddable in a standard Euclidean space because it may not be structurally rich enough. On the other hand, in many metric learning problems, a component-wise dissimilarity measure can be defined as a weighted linear convex combination and weights can be suitably learned. This article, after introducing some hints on the relation between distances and the metric learning paradigm, provides a discussion along with some experiments on how weights, intended as mathematical operators, interact with the Euclidean behavior of dissimilarity matrices.
2022
dissimilarity space; euclidean embedding; kernel methods; metric learning; pattern recognition; pseudo-euclidean embedding
01 Pubblicazione su rivista::01a Articolo in rivista
On component-wise dissimilarity measures and metric properties in pattern recognition / De Santis, Enrico; Martino, Alessio; Rizzi, Antonello. - In: PEERJ. COMPUTER SCIENCE.. - ISSN 2376-5992. - 8:(2022), pp. 1-26. [10.7717/peerj-cs.1106]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1657922
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