Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multilinear Low-Rank Tensors on Graphs & Applications   We propose a new framework for the analysis of low-rank tensors which lies at
the intersection of spectral graph theory and signal processing. As a first
step, we present a new graph based low-rank decomposition which approximates
the classical low-rank SVD for matrices and multi-linear SVD for tensors. Then,
building on this novel decomposition we construct a general class of convex
optimization problems for approximately solving low-rank tensor inverse
problems, such as tensor Robust PCA. The whole framework is named as
'Multilinear Low-rank tensors on Graphs (MLRTG)'. Our theoretical analysis
shows: 1) MLRTG stands on the notion of approximate stationarity of
multi-dimensional signals on graphs and 2) the approximation error depends on
the eigen gaps of the graphs. We demonstrate applications for a wide variety of
4 artificial and 12 real tensor datasets, such as EEG, FMRI, BCI, surveillance
videos and hyperspectral images. Generalization of the tensor concepts to
non-euclidean domain, orders of magnitude speed-up, low-memory requirement and
significantly enhanced performance at low SNR are the key aspects of our
framework.

4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math