Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accurate and fast matrix factorization for low-rank learning   In this paper, we tackle two important problems in low-rank learning, which
are partial singular value decomposition and numerical rank estimation of huge
matrices. By using the concepts of Krylov subspaces such as Golub-Kahan
bidiagonalization (GK-bidiagonalization) as well as Ritz vectors, we propose
two methods for solving these problems in a fast and accurate way. Our
experiments show the advantages of the proposed methods compared to the
traditional and randomized singular value decomposition methods. The proposed
methods are appropriate for applications involving huge matrices where the
accuracy of the desired singular values and also all of their corresponding
singular vectors are essential. As a real application, we evaluate the
performance of our methods on the problem of Riemannian similarity learning
between two various image datasets of MNIST and USPS.

6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con