Abstract
The problem of universal outlying sequence detection is studied, where the goal is to detect outlying sequences among M sequences of samples. A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequences. In the universal setting, we are interested in identifying all the outlying sequences without knowing the underlying generating distributions. We consider the outlying sequence detection problem in three different scenarios: first, known number of outlying sequences; second, unknown number of identical outlying sequences; and finally, typical and outlying distributions forming clusters. In this paper, a class of tests based on distribution clustering is proposed. These tests are shown to be exponentially consistent with linear time complexity in M. Numerical results demonstrate that our clustering-based tests achieve similar performance to existing tests, while being considerably more computationally efficient.
| Original language | English (US) |
|---|---|
| Article number | 8651359 |
| Pages (from-to) | 2115-2128 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Signal Processing |
| Volume | 67 |
| Issue number | 8 |
| DOIs | |
| State | Published - Apr 15 2019 |
Keywords
- Anomaly detection
- clustering algorithm
- exponential consistency
- outlier detection
- universal outlier hypothesis testing
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection. / Bu, Yuheng; Zou, Shaofeng; Veeravalli, Venugopal Varadachari.
In: IEEE Transactions on Signal Processing, Vol. 67, No. 8, 8651359, 15.04.2019, p. 2115-2128.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection
AU - Bu, Yuheng
AU - Zou, Shaofeng
AU - Veeravalli, Venugopal Varadachari
PY - 2019/4/15
Y1 - 2019/4/15
N2 - The problem of universal outlying sequence detection is studied, where the goal is to detect outlying sequences among M sequences of samples. A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequences. In the universal setting, we are interested in identifying all the outlying sequences without knowing the underlying generating distributions. We consider the outlying sequence detection problem in three different scenarios: first, known number of outlying sequences; second, unknown number of identical outlying sequences; and finally, typical and outlying distributions forming clusters. In this paper, a class of tests based on distribution clustering is proposed. These tests are shown to be exponentially consistent with linear time complexity in M. Numerical results demonstrate that our clustering-based tests achieve similar performance to existing tests, while being considerably more computationally efficient.
AB - The problem of universal outlying sequence detection is studied, where the goal is to detect outlying sequences among M sequences of samples. A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequences. In the universal setting, we are interested in identifying all the outlying sequences without knowing the underlying generating distributions. We consider the outlying sequence detection problem in three different scenarios: first, known number of outlying sequences; second, unknown number of identical outlying sequences; and finally, typical and outlying distributions forming clusters. In this paper, a class of tests based on distribution clustering is proposed. These tests are shown to be exponentially consistent with linear time complexity in M. Numerical results demonstrate that our clustering-based tests achieve similar performance to existing tests, while being considerably more computationally efficient.
KW - Anomaly detection
KW - clustering algorithm
KW - exponential consistency
KW - outlier detection
KW - universal outlier hypothesis testing
UR - http://www.scopus.com/inward/record.url?scp=85063258202&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063258202&partnerID=8YFLogxK
U2 - 10.1109/TSP.2019.2901364
DO - 10.1109/TSP.2019.2901364
M3 - Article
AN - SCOPUS:85063258202
VL - 67
SP - 2115
EP - 2128
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 8
M1 - 8651359
ER -