Section: Application Domains
The field of classification for complex data as curves, functions, spectra and time series is important in situations when the values of the explanatory variables of each value are functional, rather than scalar. Classic data analysis questions are being revisited to define new strategies that take the functional nature of the data into account. This new domain, functional data analysis, addresses a variety of applied problems, including longitudinal studies, analysis of fMRI data and spectral calibration.
We are focusing on classification problems with a particular emphasis on clustering, i.e. unsupervised classification. In addition to classic questions such as the choice of the number of clusters, the norm for measuring the distance between two observations, and the vectors for representing clusters, we must also address a major computational problem. The functional nature of the data requires a very large computational effort, which need to be addressed with efficient or anytime algorithms.