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Summary: AN EFFICIENT PARTICLE FILTERING TECHNIQUE ON THE GRASSMANN MANIFOLD
Quentin Rentmeesters, P.-A. Absil, Paul Van Dooren
Universit´e catholique de Louvain
B-1348 Louvain-la-Neuve, Belgium
Kyle Gallivan, Anuj Srivastava
Florida State University
Tallahassee, FL, USA 32306
ABSTRACT
Subspace tracking methods are widespread in signal and
image processing. To reduce the influence of perturbations
or outliers on the measurements, some authors have used a
stochastic piecewise constant velocity model on the Grass-
mann manifold. This paper presents an efficient way to
simulate such a model using a particular representation of
the Grassmann manifold. By doing so, we can reduce the
spatial and time complexity of filtering techniques based on
this model. We also propose an approximation of this system
which can be computed in a finite number of operations and
show similar results if the subspace variation is slow.
Index Terms-- time-varying subspace learning, Grass-
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