Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning from an Auxiliary Discriminative Task for Unsupervised
  Anomaly Detection   Unsupervised anomaly detection from high dimensional data like mobility
networks is a challenging task. Study of different approaches of feature
engineering from such high dimensional data have been a focus of research in
this field. This study aims to investigate the transferability of features
learned by network classification to unsupervised anomaly detection. We propose
use of an auxiliary classification task to extract features from unlabelled
data by supervised learning, which can be used for unsupervised anomaly
detection. We validate this approach by designing experiments to detect
anomalies in mobility network data from New York and Taipei, and compare the
results to traditional unsupervised feature learning approaches of PCA and
autoencoders. We find that our feature learning approach yields best anomaly
detection performance for both datasets, outperforming other studied
approaches. This establishes the utility of this approach to feature
engineering, which can be applied to other problems of similar nature.

19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an