Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Analysis using Riemannian Geometry and Applications to Chemical
  Engineering   We explore the use of tools from Riemannian geometry for the analysis of
symmetric positive definite matrices (SPD). An SPD matrix is a versatile data
representation that is commonly used in chemical engineering (e.g.,
covariance/correlation/Hessian matrices and images) and powerful techniques are
available for its analysis (e.g., principal component analysis). A key
observation that motivates this work is that SPD matrices live on a Riemannian
manifold and that implementing techniques that exploit this basic property can
yield significant benefits in data-centric tasks such classification and
dimensionality reduction. We demonstrate this via a couple of case studies that
conduct anomaly detection in the context of process monitoring and image
analysis.

32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom