Codes

    • OP-SVM

Mex/Matlab code of one-plus-class SVM (OP-SVM) for classifying highly imbalanced data. Generates the figures in the paper.

      • G. Lee, H. Gurm and Z. Syed, "Predicting Complications of Percutaneous Coronary Intervention using a Novel Support Vector Method"


    • TCEM

EM algorithm for fitting a multivariate Gaussian mixture model with truncated and censored data.

      • G. Lee and C. Scott, "EM algorithms for multivariate Gaussian mixture models with truncated and censored data"


    • Cluster nearest neighbor

Algorithm for file matching, and associated EM algorithm for fitting a mixture of PPCA model with missing attributes.

      • G. Lee, W. Finn and C. Scott, "Statistical file matching of flow cytometry data"


    • Nested support vector machines

Matlab code to generate cost-sensitive and one-class SVMs that are properly nested (unlike standard SVMS) as the cost-asymmetry or density level parameter is varied. The solution paths are piecewise linear with a user-selected number of breakpoints.

      • G. Lee and C. Scott, "Nested support vector machines"


    • OC-SVM LST

Matlab code to estimate density level set tree using the One-Class SVM (OC-SVM). This algorithm uses the OC-SVM solution path algorithm below.


    • OC-SVM HC

Matlab code for hierarchical clustering using the One-Class SVM (OC-SVM). This algorithm uses the OC-SVM solution path algorithm below. The result of the hierarchical clustering is visualized with dendrograms and spanning trees.


    • SVM path algorithms

Matlab code to generate solution paths for the cost-sensitive SVM with varying cost-asymmetry, and the one-class SVM with varying density level parameter. The algorithms were inspired by the path algorithm of Hastie et al., which varies a regularization parameter, and were implemented for comparison with the nested SVM code above. The CS-SVM algorithm is different from the one developed by Bach et al. in that we capture the cost asymmetry in a single parameter. The OC-SVM path algorithm was detailed here:

      • G. Lee and C. Scott, "The one class support vector machine solution path"