In many situations, dissimilarities between objects cannot be measured directly, but have to be constructed from some known characteristics of the objects of interest, e.g. some values on certain variables. From a philosophical point of view, the assumption of the objective existence of a “true” but not directly observable dissimilarity value between two objects is highly questionable. Therefor...

This document is provided as an electronic appendix to the article “A new dissimilarity measure for finding semantic structure in category fluency data with implications for understanding memory organization in schizophrenia”. This appendix contains two parts. In the first we derive a measure of the distance between two items in a fluency list that is appropriately normalized for list-length. T...

In most content-based image retrieval systems, the low level visual features such as color, texture and region play an important role. Variety of dissimilarity measures were introduced for an uniform quantization of visual features, or a histogram. However, a cluster-based representation, or a signature, has proven to be more compact and theoretically sound for the accuracy and robustness than ...

This paper presents an empirical evaluation on a dissimilarity measure strategy by which dissimilarity-based classifications (DBCs) [10] can be efficiently implemented. In DBCs, classifiers are not based on the feature measurements of individual objects, but rather on a suitable dissimilarity measure among the objects. In image classification tasks, however, one of the most intractable problems...

We present a quantitative approach to the measurement of shape dissimilarity between two 3D (three-dimensional) curves. Any 3D continuous curve can be digitalized and represented as a 3D discrete curve. Thus, a 3D discrete curve is composed of constant orthogonal straightline segments. In order to represent 3D discrete curves, we use the orthogonal direction change chain code. The chain element...

Distance-based methods in pattern recognition and machine learning have to rely on a similarity or dissimilarity measure between patterns in the input space. For many applications, Euclidean distance in the input space is not a good choice and hence more complicated distance metrics have to be used. In this paper, we propose a parametric method for metric learning based on class label informati...

We use ensembles of proximity based one-class classifiers for authorship verification task. The one-class classifiers compare, for each document of the known authorship, the dissimilarity between this document and the most dissimilar other document of this authorship to the dissimilarity between this document and the questioned document. As the dissimilarity measure between documents we use Com...