Dynamic Data Factorization
We present a technique for reducing the dimensionality of a s tationary time series with an arbitrary covariance sequenc e. This problem is solved in closed form in a way that is asymptot ically efficient (i.e. optimal in the sense of maximum likeli hood). Our technique can be seen as a special case of a more general th ory of subspace identification borrowed from the field of dynamical systems. Although our scheme only captures secon d-order statistics, we suggest extensions to model higherorder moments. Our technique can be used to perform factorization under the constraint that one of the factors evolves accordi ng to a dynamical model. Applications range from structure fro m motion to the analysis of dynamic textures.