Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High Dimensional Process Monitoring Using Robust Sparse Probabilistic
  Principal Component Analysis   High dimensional data has introduced challenges that are difficult to address
when attempting to implement classical approaches of statistical process
control. This has made it a topic of interest for research due in recent years.
However, in many cases, data sets have underlying structures, such as in
advanced manufacturing systems. If extracted correctly, efficient methods for
process control can be developed. This paper proposes a robust sparse
dimensionality reduction approach for correlated high-dimensional process
monitoring to address the aforementioned issues. The developed monitoring
technique uses robust sparse probabilistic PCA to reduce the dimensionality of
the data stream while retaining interpretability. The proposed methodology
utilizes Bayesian variational inference to obtain the estimates of a
probabilistic representation of PCA. Simulation studies were conducted to
verify the efficacy of the proposed methodology. Furthermore, we conducted a
case study for change detection for in-line Raman spectroscopy to validate the
efficiency of our proposed method in a practical scenario.

2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph