Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense
  Stationary Ergodic Processes   We introduce a new unsupervised learning problem: clustering wide-sense
stationary ergodic stochastic processes. A covariance-based dissimilarity
measure together with asymptotically consistent algorithms is designed for
clustering offline and online datasets, respectively. We also suggest a formal
criterion on the efficiency of dissimilarity measures, and discuss of some
approach to improve the efficiency of our clustering algorithms, when they are
applied to cluster particular type of processes, such as self-similar processes
with wide-sense stationary ergodic increments. Clustering synthetic data and
real-world data are provided as examples of applications.

37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ