In this paper we demonstrate that a psychoacoustic model-based distance measure performs better than a speech signal distance measure in assessing the pronunciation of individual foreign speakers. The experiments show that the perceptualbased method performs not only quantitatively better than a speech spectrum-based method, but also qualitatively better, hence showing that auditory information...

Although the “scale-free” literature is large and growing, it gives neither a precise definition of scale-free graphs nor rigorous proofs of many of their claimed properties. In fact, it is easily shown that the existing theory has many inherent contradictions and verifiably false claims. In this paper, we propose a new, mathematically precise, and structural definition of the extent to which a...

This paper presents an empirical evaluation on a dissimilarity measure strategy by which dissimilarity-based classifications (DBC) can be implemented efficiently. In DBC, classification is not based on feature measurements of individual objects (a set of attributes), but rather on a suitable dissimilarity measure among the individual objects (pair-wise object comparisons). One problem of DBC is...

Person re-identification is the task of recognizing an individual that has already been observed over a network of video-surveillance cameras. Methods proposed in literature so far addressed this issue as a classical matching problem: a descriptor is built directly from the view of the person, and a similarity measure between descriptors is defined accordingly. In this work, we propose a genera...

The paper deals with the well-known notion of (dis)similarity measures between fuzzy sets. We provide three separate lists of axioms that fit with the respective notions of “general comparison measure”, “similarity measure” and “dissimilarity measure”. Then we review some of the most important axiomatic definitions of (dis)similarity measures in the literature, by referring to the axioms in tho...

We propose a measure of divergence of probability distributions for quantifying the dissimilarity of two chaotic attractors. This measure is defined in terms of a generalized entropy. We illustrate our procedure by considering the effect of additive noise in the well known Hénon attractor. Finally, we show how our approach allows one to detect nonstationary events in a time series.

The most widely used measure of segregation is the dissimilarity index, D. It is now well understood that this measure also reflects randomness in the allocation of individuals to units; that is, it measures deviations from evenness not deviations from randomness. This leads to potentially large values of the segregation index when unit sizes and/or minority proportions are small, even if there...

K nearest neighbors (KNN) are known as one of the simplest nonparametric classifiers but in high dimensional setting accuracy of KNN are affected by nuisance features. In this study, we proposed the K important neighbors (KIN) as a novel approach for binary classification in high dimensional problems. To avoid the curse of dimensionality, we implemented smoothly clipped absolute deviation (SCAD...

The most widely used measure of segregation is the so-called dissimilarity index. It is now well understood that this measure also reflects randomness in the allocation of individuals to units (i.e. it measures deviations from evenness, not deviations from randomness). This leads to potentially large values of the segregation index when unit sizes and/or minority proportions are small, even if ...