Non-parametric distance-based classification techniques and their applications

Abstract

Pattern Recognition is a key scientific field of longstanding tradition, with origins in the early years of computer science. Today, Pattern Recognition has reached a level of maturity that allows us to build highly sophisticated systems, which perform very different tasks. Nevertheless, its evolution has opened up a number of new problems, ranging from specific algorithms to system integration, which remain elusive and assure a long life for this research field. The field is progressing rapidly, and an air of excitement among researchers is being created by the increasing scope of applications to which Pattern Recognition is relevant, and by the many technical advances that have been made in recent years. One reason Pattern Recognition is such a rapidly developing field lies in the fact that modern societies have entered the ‘‘data era.’’ An unprecedented investment is being made in the collection of data, with archives being formed on an enormous scale. Biological data are being acquired using increasingly fast machines to scan genomes, hyperspectral satellite imagery is being stored on a massive scale, and web documents are appearing at an explosive rate in internet, and so on. The development of effective ways for extracting useful information from these data stores is an overall challenge to computer science as a discipline. This goal drives much of present pattern recognition and machine learning research. This Special Issue aims to provide a platform for Pattern Recognition researchers to present their newly developed techniques and applications in the area of non-parametric Pattern Recognition. From a practical point of view, nonparametric classification is one of the most relevant classification approaches for real world problems. From a theoretical point of view, it is still a very active area of research in spite of the fact of being a classical topic of the field. Areas of interest for this special journal issue included, but were not limited to the following topics: distance based classification in metric and non metric spaces, feature selection and extraction, prototype selection, editing and condensing, non supervised distance-based classification, and applications. We received 45 manuscripts. Fifteen manuscripts were finally selected for this Special Issue after being reviewed by at least three external reviewers. The accepted papers can be broadly thought of as being representative of the breadth of concerns in areas like discriminant analysis, advanced nearest neighbour classification, online learning, dissimilarity-based classification, and covering a wide range of application domains. The paper ‘‘Distance-based Discriminant Analysis Method and its Applications’’ by Kosinov and Pun presents a new approach for finding discriminative linear transformations from inter-observation distances. The proposed method is suitable for both binary and multiple category problems and allows for a kernel-based extension that overcomes the linearity assumption of the initial formulation. F. Pla (&) Department de Llenguatges i Sistemes Informatics, Universitat Jaume I, Castello, Spain e-mail: Filiberto.Pla@lsi.uji.es