Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Sensor Network Stimulation Model Identification via Gaussian
  Mixture Model and Deep Embedded Features   Visual sensor networks constitute a fundamental class of distributed sensing
systems, with unique complexity and performance research subjects. One of these
novel challenges is represented by the identification of the network
stimulation model (SM), which emerges when a set of detectable events trigger
different subsets of the cameras. In this direction, the formulation of the
related SM identification problem is proposed, along with a proper network
observations generation method. Consequently, an approach based on deep
embedded features and soft clustering is leveraged to solve the presented
identification problem. In detail, the Gaussian Mixture Modeling is employed to
provide a suitable description for data distribution and an autoencoder is used
to reduce undesired effects due to the so-called curse of dimensionality.
Hence, it is shown that a SM can be learnt by solving Maximum A-Posteriori
estimation on the encoded features belonging to a space with lower
dimensionality. Lastly, numerical results are reported to validate the devised
estimation algorithm.

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The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn