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By Vito Volterra

Volterra V. Lecons sur les fonctions de lignes (fr)(1913)(ISBN 1429702923)

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Is it possible to guarantee some asymptotically optimal result? At this point, it is relevant to realize the following. If objects show a zero distance if and only if they are identical and if they are labeled unambiguously then classes do not overlap and a zero-error classifier is possible. What is the best way to reach this? Most classifiers assume class overlap. The study of classifiers that make use of the fact that classes do not overlap didn’t make much progress after the definition of the original perceptron rule.

Xn } be a training set. Given a dissimilarity function and/or dissimilarity data, we define a data-dependent mapping D(·, R) : X → Rk from X to 2 Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness 21 the so-called dissimilarity space (DS) [19, 26, 43]. The k-element set R consists of objects that are representative for the problem. This set is called the representation set or prototype set and it may be a subset of X . In the dissimilarity space, each dimension D(·, pi ) describes a dissimilarity to a prototype pi from R.

The section below explains how to find the embedded PES. 1 Pseudo-Euclidean Embedded Space A symmetric dissimilarity matrix D := D(X , X ) can be embedded in a PseudoEuclidean Space (PES) E by an isometric mapping [24, 55]. The embedding relies on the indefinite Gram matrix G, derived as G := − 12 H D 2 H , where D 2 = (dij2 ) and H = I − n1 11T is the centering matrix. H projects the data such that X has a zero mean vector. The eigendecomposition of G leads to G = QΛQT = 1 1 Q|Λ| 2 [Jpq ; 0]|Λ| 2 QT , where Λ is a diagonal matrix of eigenvalues, first decreasing p positive ones, then increasing q negative ones, followed by zeros.

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