Kernel dependence analysis and graph structure morphing for novelty detection with high-dimensional small size data set
Name
Kernel Dependence Novelty detection _ Reza Mohammadi.pdf
Description
Accepted version
Size
20.18 MB
Format
Adobe PDF
Checksum (MD5)
65056a323d37046a8282c61c999b3847
Author(s)
Mohammadi Ghazi Mahalleh, Reza
Welsch, Roy E
Buyukozturk, Oral
Date Issued
September 2020
Journal
Mechanical Systems and Signal Processing
Publisher
Elsevier BV
Citation
Mohammadi-Ghazi, Reza et al. "Kernel dependence analysis and graph structure morphing for novelty detection with high-dimensional small size data set." Mechanical Systems and Signal Processing 143 (September 2020): 106775 © 2020 Elsevier Ltd
Version
Author's final manuscript
Abstract
In this study, we propose a new approach for novelty detection that uses kernel dependence techniques for characterizing the statistical dependencies of random variables (RV) and use this characterization as a basis for making inference. Considering the statistical dependencies of the RVs in multivariate problems is an important challenge in novelty detection. Ignoring these dependencies, when they are strong, may result in inaccurate inference, usually in the form of high false positive rates. Previously studied methods, such as graphical models or conditional classifiers, mainly use density estimation techniques as their main learning element to characterize the dependencies of the relevant RVs. Therefore, they suffer from the curse of dimensionality which makes them unable to handle high-dimensional problems. The proposed method, however, avoids using density estimation methods, and rather, employs a kernel method, which is robust with respect to dimensionality, to encode the dependencies and hence, it can handle problems with arbitrarily high-dimensional data. Furthermore, the proposed method does not need any prior information about the dependence structure of the RVs; thus, it is applicable to general novelty detection problems with no simplifying assumption. To test the performance of the proposed method, we apply it to realistic application problems for analyzing sensor networks and compare the results to those obtained by peer methods.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Sloan School of Management
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.ymssp.2020.106775
