In this paper we introduce a nonextensive quantum information theoretic measure which may be defined between any arbitrary number of density matrices, and we analyze its fundamental properties in the spectral graph-theoretic framework. Unlike other entropic measures, the proposed quantum divergence is symmetric, matrix-convex, theoretically upper-bounded, and has the advantage of being generali...

The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. We propose an integrated process verifying artificial neural network (ANN) classifiers. This consists off-line on-line performance prediction phase. intended to verify ANN classifier generalisation performance, this end makes use dataset dissimilarity measur...

Diet guidelines recommend increasing dietary diversity. Yet, metrics for dietary diversity have neither been well-defined nor evaluated for impact on metabolic health. Also, whether diversity has effects independent of diet quality is unknown. We characterized and evaluated associations of diet diversity and quality with abdominal obesity and type II diabetes (T2D) in the Multi-Ethnic Study of ...

— A new method to detect words that are likely to be confused by speech recognition systems is presented in this paper. A new dissimilarity measure between two words is calculated in two steps. Firstly, the phonetic transcriptions of the words are aligned using only phonetic information. Two kinds of alignments are used: either with or without insertions and deletions. Secondly, the dissimilari...

In this work, we address the problem of defining a robust patch dissimilarity measure for an image corrupted by an additive white Gaussian noise. The whiteness of the noise, despite being a common assumption that is realistic for RAW images, is hardly used to its full potential by classical denoising methods. In particular, the L-norm is very widely used to evaluate distances and similarities b...

Recent development of high-resolution single-nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. However, SNP data typically has a large number of SNPs (e.g., 400 thousand SNPs in genome-wide Parkinson disease SNP data) and a few hundred of samples. Conventional classification methods may not be effective when applied to such genome-wide SNP d...

A measure of dissimilarity (distance) is proposed for comparing origami crease patterns represented as geometric graphs. The distance measure is determined by minimum-weight matchings calculated between the edges as well as the vertices of the graphs being compared. The distances between pairs of edges and pairs of vertices of the graph are weighted linear combinations of six parameters that co...

Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of feature vectors (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space, whereas negative bags only contain negative instances. The classes in a MIL problem are therefore not treated in the...

A large number of diversity measures have been proposed in the literature, almost all which are designed for sets with elements that differ multiple aspects. However, these types not appropriate a single aspect only, such as duration, value or probability. In this essay I present new measure diversity, specifically diversity. The captures intuitive idea is affected by maximal dissimilarity betw...