Support vector data description (SVDD) is a popular anomaly detection technique.
Each iteration involves only the existing support vectors and the new data point.
Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data.
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.
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.
In this paper the exact linear relation between the leading eigenvector of the unnormalized modularity matrix and the eigenvectors of the adjacency matrix is developed.