Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Matrix Approximation under Local Low-Rank Assumption   Matrix approximation is a common tool in machine learning for building
accurate prediction models for recommendation systems, text mining, and
computer vision. A prevalent assumption in constructing matrix approximations
is that the partially observed matrix is of low-rank. We propose a new matrix
approximation model where we assume instead that the matrix is only locally of
low-rank, leading to a representation of the observed matrix as a weighted sum
of low-rank matrices. We analyze the accuracy of the proposed local low-rank
modeling. Our experiments show improvements in prediction accuracy in
recommendation tasks.

3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th