Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of
  Unseen Multivariate Time-series   There exist many approaches for description and recognition of unseen classes
in datasets. Nevertheless, it becomes a challenging problem when we deal with
multivariate time-series (MTS) (e.g., motion data), where we cannot apply the
vectorial algorithms directly to the inputs. In this work, we propose a novel
multiple-kernel dictionary learning (MKD) which learns semantic attributes
based on specific combinations of MTS dimensions in the feature space. Hence,
MKD can fully/partially reconstructs the unseen classes based on the training
data (seen classes). Furthermore, we obtain sparse encodings for unseen classes
based on the learned MKD attributes, and upon which we propose a simple but
effective incremental clustering algorithm to categorize the unseen MTS classes
in an unsupervised way. According to the empirical evaluation of our MKD
framework on real benchmarks, it provides an interpretable reconstruction of
unseen MTS data as well as a high performance regarding their online
clustering.

34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ