Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Direct l_(2,p)-Norm Learning for Feature Selection   In this paper, we propose a novel sparse learning based feature selection
method that directly optimizes a large margin linear classification model
sparsity with l_(2,p)-norm (0 < p < 1)subject to data-fitting constraints,
rather than using the sparsity as a regularization term. To solve the direct
sparsity optimization problem that is non-smooth and non-convex when 0<p<1, we
provide an efficient iterative algorithm with proved convergence by converting
it to a convex and smooth optimization problem at every iteration step. The
proposed algorithm has been evaluated based on publicly available datasets, and
extensive comparison experiments have demonstrated that our algorithm could
achieve feature selection performance competitive to state-of-the-art
algorithms.

16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph