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Found 6 papers, 1 papers with code
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel
Each iteration involves only the existing support vectors and the new data point.
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.
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.
Modularity Component Analysis versus Principal Component Analysis
In this paper the exact linear relation between the leading eigenvectors of the modularity matrix and the singular vectors of an uncentered data matrix is developed.
Relations Between Adjacency and Modularity Graph Partitioning
This paper develops the exact linear relationship between the leading eigenvector of the unnormalized modularity matrix and the eigenvectors of the adjacency matrix.
The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description
Support vector data description (SVDD) is a popular anomaly detection technique.
