Sampling Method for Fast Training of Support Vector Data Description

06/16/2016
by   Arin Chaudhuri, et al.
0

Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data. SVDD computation time is high for large training datasets which limits its use in big-data process-monitoring applications. We propose a new iterative sampling-based method for SVDD training. The method incrementally learns the training data description at each iteration by computing SVDD on an independent random sample selected with replacement from the training data set. The experimental results indicate that the proposed method is extremely fast and provides a good data description .

READ FULL TEXT
research
11/15/2018

The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description

Support vector data description (SVDD) is a popular anomaly detection te...
research
08/16/2017

The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description

Support vector data description (SVDD) is a popular technique for detect...
research
09/29/2020

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Support Vector Data Description (SVDD) is a popular one-class classifier...
research
05/31/2020

Applying support vector data description for fraud detection

Fraud detection is an important topic that applies to various enterprise...
research
07/25/2016

A Non-Parametric Control Chart For High Frequency Multivariate Data

Support Vector Data Description (SVDD) is a machine learning technique u...
research
09/18/2020

Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change

Biology is both an important application area and a source of motivation...
research
10/31/2016

Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data

Support Vector Data Description (SVDD) provides a useful approach to con...

Please sign up or login with your details

Forgot password? Click here to reset