Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Natural coordinate descent algorithm for L1-penalised regression in
  generalised linear models   The problem of finding the maximum likelihood estimates for the regression
coefficients in generalised linear models with an L1 sparsity penalty is shown
to be equivalent to minimising the unpenalised maximum log-likelihood function
over a box with boundary defined by the L1-penalty parameter. In one-parameter
models or when a single coefficient is estimated at a time, this result implies
a generic soft-thresholding mechanism which leads to a novel coordinate descent
algorithm for generalised linear models that is entirely described in terms of
the natural formulation of the model and is guaranteed to converge to the true
optimum. A prototype implementation for logistic regression tested on two
large-scale cancer gene expression datasets shows that this algorithm is
efficient, particularly so when a solution is computed at set values of the
L1-penalty parameter as opposed to along a regularisation path. Source code and
test data are available from http://tmichoel.github.io/glmnat/.

31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin