Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Tracking of Sparse Signals via an Earth Mover's Distance
  Dynamics Regularizer   Tracking algorithms such as the Kalman filter aim to improve inference
performance by leveraging the temporal dynamics in streaming observations.
However, the tracking regularizers are often based on the $\ell_p$-norm which
cannot account for important geometrical relationships between neighboring
signal elements. We propose a practical approach to using the earth mover's
distance (EMD) via the earth mover's distance dynamic filtering (EMD-DF)
algorithm for causally tracking time-varying sparse signals when there is a
natural geometry to the coefficient space that should be respected (e.g.,
meaningful ordering). Specifically, this paper presents a new Beckmann
formulation that dramatically reduces computational complexity, as well as an
evaluation of the performance and complexity of the proposed approach in
imaging and frequency tracking applications with real and simulated
neurophysiology data.

18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga