Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Riemannian Approach for Constrained Optimization Model in Extreme
  Classification Problems   We propose a novel Riemannian method for solving the Extreme multi-label
classification problem that exploits the geometric structure of the sparse
low-dimensional local embedding models. A constrained optimization problem is
formulated as an optimization problem on matrix manifold and solved using a
Riemannian optimization method. The proposed approach is tested on several real
world large scale multi-label datasets and its usefulness is demonstrated
through numerical experiments. The numerical experiments suggest that the
proposed method is fastest to train and has least model size among the
embedding-based methods. An outline of the proof of convergence for the
proposed Riemannian optimization method is also stated.

13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat