  In this paper, a new algorithm for extracting features from sequences of
multidimensional observations is presented. The independently developed Dynamic
Mode Decomposition and Matrix Pencil methods provide a least-squares
model-based approach for estimating complex frequencies present in signals as
well as their corresponding amplitudes. Unlike other feature extraction methods
such as Fourier Transform or Autoregression which have to be computed for each
sequence individually, the least-squares approach considers the whole dataset
at once. It invokes order reduction methods to extract a small number of
features best describing all given data, and indicate which frequencies
correspond to which sequences. As an illustrative example, the new method is
applied to regions of different grain orientation in a Transmission Electron
Microscopy image.
