Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Jitter-Adaptive Dictionary Learning - Application to Multi-Trial
  Neuroelectric Signals   Dictionary Learning has proven to be a powerful tool for many image
processing tasks, where atoms are typically defined on small image patches. As
a drawback, the dictionary only encodes basic structures. In addition, this
approach treats patches of different locations in one single set, which means a
loss of information when features are well-aligned across signals. This is the
case, for instance, in multi-trial magneto- or electroencephalography (M/EEG).
Learning the dictionary on the entire signals could make use of the alignement
and reveal higher-level features. In this case, however, small missalignements
or phase variations of features would not be compensated for. In this paper, we
propose an extension to the common dictionary learning framework to overcome
these limitations by allowing atoms to adapt their position across signals. The
method is validated on simulated and real neuroelectric data.

29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio