The effectiveness of their method for anomaly detection was demonstrated on four benchmark datasets, and results have showed satisfactory performance.
In other words, the low-dimensional signal contained the main features of the original signal, so CS theory can provide an effective method for anomaly detection in big data.
Application of methods for geochemical anomaly recognition based on classical statistics can, thus, be misguided and potentially result in spurious anomalies.
To verify the effectiveness of the proposed method, the real-world energy data coming from a steel plant are employed to perform the experiments, and the results indicate that the proposed method exhibits a higher precision compared to the other methods for the anomaly detection.
The research on new methods for sketching, anomaly detection, noise removal, feature extraction, outliers detection, and pre-filtering of streaming data is required to reduce big data effectively.
Gamma-ray spectrometry is one of the most cost-effective prospecting methods for investigating anomalies in potassium, uranium, thorium and alteration indexes associated with hydrothermal and radioactive ore deposits.
On the basis of the theory of Tilt angle and Tilt-depth, we derive new Tilt-depth method for gravity anomaly from the forward formula of the upright dyke model, namely Tilt-depth2.
A simple analysis method for orbit anomaly detection, called semi-major axis change method (SACM) was presented by using a relationship between the change of orbit parameters and velocity increments.
Recently, several methods for target or anomaly detection that rely on recovering the full spatiospectral data from projection measurements[36, 37] have been proposed.
