12 Aprile, 2013 14:30 in punto

Functional Factor Analysis For Periodic Remote Sensing Data

Surajit Ray, University of Glasgow

Aula Seminari F. Saleri VI Piano MOX- Dipartimento di Matematica, Politecnico di Milano

We present a new approach to factor rotation for functional data. This rotation is achieved by rotating the functional principal components towards a pre-defined space of periodic functions designed to decompose the total variation into components that are nearlyperiodic and nearly-aperiodic with a pre-defined period. We show that the factor rotation can be obtained by calculation of canonical correlations between appropriate spaces which makes the methodology computationally efficient. Moreover we demonstrate that our proposed rotations provide stable and interpretable results in the presence of highly complex covariance. This work is motivated by the goal of finding interpretable sources of variability in vegetation index obtained from remote sensing instruments and we demonstrate our methodology through an application of factor rotation of this data.

contact: laura.sangalli@polimi.it