On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

2006 ◽  
Vol 6 (3) ◽  
pp. 770-783 ◽  
Author(s):  
A. Perera ◽  
N. Papamichail ◽  
N. Barsan ◽  
U. Weimar ◽  
S. Marco
Keyword(s):  
Principal Component Analysis ◽  
Gas Sensor ◽  
Novelty Detection ◽  
Sensor Arrays ◽  
Principal Component ◽  
Component Analysis ◽  
Dynamic Principal Component Analysis ◽  
On Line ◽  
Drift Conditions ◽  
Gas Sensor Arrays

Adaptive Principal Component Analysis-Based Outliers Detection Through Neighborhood Voting in Wireless Sensor Networks

2019 ◽  
pp. 255-285
Author(s):  
Ekaterina Aleksandrova ◽  
Christos Anagnostopoulos
Keyword(s):  
Principal Component Analysis ◽  
Statistical Learning ◽  
Variance Estimation ◽  
Principal Component ◽  
Component Analysis ◽  
Power Method ◽  
Environmental Sensors ◽  
On Line ◽  
Computing Environments ◽  
Adaptive Principal Component Analysis

This chapter introduces statistical learning methods and findings of a group decision-making algorithm in internet of things (IoT) and edge computing environments. The discussed methodology locally detects outliers in an on-line and adaptive mode. It is driven by three perspectives—opinion, confidence, and independence—and exploits the incremental principal component analysis using the power method for eigenvector and eigenvalue estimation and Knuth and Welford's online algorithms for variance estimation. The methodology is implemented and evaluated over real contextual data in a wireless network of environmental sensors from where appropriate conclusions are drawn about the capabilities and limitations of the proposed solution in IoT environments.

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