|Title||Bidirectional discrimination with application to data visualization.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Huang, Hanwen, Yufeng Liu, and J S. Marron|
|Date Published||2012 Dec|
Linear classifiers are very popular, but can have limitations when classes have distinct subpopulations. General nonlinear kernel classifiers are very flexible, but do not give clear interpretations and may not be efficient in high dimensions. We propose the bidirectional discrimination classification method, which generalizes linear classifiers to two or more hyperplanes. This new family of classification methods gives much of the flexibility of a general nonlinear classifier while maintaining the interpretability, and much of the parsimony, of linear classifiers. They provide a new visualization tool for high-dimensional, low-sample-size data. Although the idea is generally applicable, we focus on the generalization of the support vector machine and distance-weighted discrimination methods. The performance and usefulness of the proposed method are assessed using asymptotics and demonstrated through analysis of simulated and real data. Our method leads to better classification performance in high-dimensional situations where subclusters are present in the data.
|Original Publication||Bidirectional discrimination with application to data visualization.|
|PubMed Central ID||PMC3629858|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P50 CA058223 / CA / NCI NIH HHS / United States
R01 CA149569 / CA / NCI NIH HHS / United States
Bidirectional discrimination with application to data visualization.