Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SMATE: Semi-Supervised Spatio-Temporal Representation Learning on
  Multivariate Time Series   Learning from Multivariate Time Series (MTS) has attracted widespread
attention in recent years. In particular, label shortage is a real challenge
for the classification task on MTS, considering its complex dimensional and
sequential data structure. Unlike self-training and positive unlabeled learning
that rely on distance-based classifiers, in this paper, we propose SMATE, a
novel semi-supervised model for learning the interpretable Spatio-Temporal
representation from weakly labeled MTS. We validate empirically the learned
representation on 30 public datasets from the UEA MTS archive. We compare it
with 13 state-of-the-art baseline methods for fully supervised tasks and four
baselines for semi-supervised tasks. The results show the reliability and
efficiency of our proposed method.

22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg