Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sinkhorn Divergence of Topological Signature Estimates for Time Series
  Classification   Distinguishing between classes of time series sampled from dynamic systems is
a common challenge in systems and control engineering, for example in the
context of health monitoring, fault detection, and quality control. The
challenge is increased when no underlying model of a system is known,
measurement noise is present, and long signals need to be interpreted. In this
paper we address these issues with a new non parametric classifier based on
topological signatures. Our model learns classes as weighted kernel density
estimates (KDEs) over persistent homology diagrams and predicts new trajectory
labels using Sinkhorn divergences on the space of diagram KDEs to quantify
proximity. We show that this approach accurately discriminates between states
of chaotic systems that are close in parameter space, and its performance is
robust to noise.

8
The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
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The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex