Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analyzing sparse dictionaries for online learning with kernels   Many signal processing and machine learning methods share essentially the
same linear-in-the-parameter model, with as many parameters as available
samples as in kernel-based machines. Sparse approximation is essential in many
disciplines, with new challenges emerging in online learning with kernels. To
this end, several sparsity measures have been proposed in the literature to
quantify sparse dictionaries and constructing relevant ones, the most prolific
ones being the distance, the approximation, the coherence and the Babel
measures. In this paper, we analyze sparse dictionaries based on these
measures. By conducting an eigenvalue analysis, we show that these sparsity
measures share many properties, including the linear independence condition and
inducing a well-posed optimization problem. Furthermore, we prove that there
exists a quasi-isometry between the parameter (i.e., dual) space and the
dictionary's induced feature space.

29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc