Classification of three-way data by the dissimilarity representation
Representation of objects by multi-dimensional data arrays has become very common for many research areas e.g. image analysis, signal processing and chemometrics. In most cases, it is the straightforward representation obtained from sophisticated measurement equipments e.g. radar signal processing. Although the use of this complex data structure could be advantageous for a better discrimination between different classes of objects, it is usually ignored. Classification tools that take this structure into account have hardly been developed yet. Meanwhile, the dissimilarity representation has demonstrated advantages in the solution of classification problems e.g. spectral data. Dissimilarities also allow the representation of multi-dimensional objects in a way that the data structure can be used. This paper introduces their use as a tool for classifying objects originally represented by two-dimensional (2D) arrays. 2D measures can be useful to achieve this representation. A 2D measure to compute the dissimilarity representation from spectral data with this kind of structure is proposed. It is compared to existent 2D measures, in terms of the information that is taken into account and computational complexity.
classification dissimilarity representation multi-way data