Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Subsampled online matrix factorization with convergence guarantees   We present a matrix factorization algorithm that scales to input matrices
that are large in both dimensions (i.e., that contains morethan 1TB of data).
The algorithm streams the matrix columns while subsampling them, resulting in
low complexity per iteration andreasonable memory footprint. In contrast to
previous online matrix factorization methods, our approach relies on
low-dimensional statistics from past iterates to control the extra variance
introduced by subsampling. We present a convergence analysis that guarantees us
to reach a stationary point of the problem. Large speed-ups can be obtained
compared to previous online algorithms that do not perform subsampling, thanks
to the feature redundancy that often exists in high-dimensional settings.

31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
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The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph