Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: First and Second Order Methods for Online Convolutional Dictionary
  Learning   Convolutional sparse representations are a form of sparse representation with
a structured, translation invariant dictionary. Most convolutional dictionary
learning algorithms to date operate in batch mode, requiring simultaneous
access to all training images during the learning process, which results in
very high memory usage and severely limits the training data that can be used.
Very recently, however, a number of authors have considered the design of
online convolutional dictionary learning algorithms that offer far better
scaling of memory and computational cost with training set size than batch
methods. This paper extends our prior work, improving a number of aspects of
our previous algorithm; proposing an entirely new one, with better performance,
and that supports the inclusion of a spatial mask for learning from incomplete
data; and providing a rigorous theoretical analysis of these methods.

17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga