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"