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Found 11 papers, 2 papers with code
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data
Sensor data analysis plays a key role in health assessment of critical equipment.
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection
In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure.
The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description
Support vector data description (SVDD) is a popular anomaly detection technique.
A New SVDD-Based Multivariate Non-parametric Process Capability Index
Process capability index (PCI) is a commonly used statistic to measure ability of a process to operate within the given specifications or to produce products which meet the required quality specifications.
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel
Each iteration involves only the existing support vectors and the new data point.
The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description
The computation of the SVDD classifier requires a kernel function, and the Gaussian kernel is a common choice for the kernel function.
Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data
When training datasets are large, the time required to obtain the optimal value of the Gaussian kernel bandwidth parameter according to Peak method can become prohibitively large.
Leveraging Unstructured Data to Detect Emerging Reliability Issues
However, there is a delay between the time of a customer complaint and the time of a failure or a claim.
A Non-Parametric Control Chart For High Frequency Multivariate Data
The non-parametric K-chart provides an attractive alternative to the traditional control charts such as the Hotelling's $T^2$ charts when distribution of the underlying multivariate data is either non-normal or is unknown.
Sampling Method for Fast Training of Support Vector Data Description
Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data.
Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description
For example, it is observed that with a Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly.
