Canonical Correlation Forests

Understand

We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification and regression.

• Individual canonical correlation trees are binary decision trees with hyperplane splits based on local canonical correlation coefficients calculated during training.
• Unlike axis-aligned alternatives, the decision surfaces of CCFs are not restricted to the coordinate system of the inputs features and therefore more naturally represent data with correlated inputs.
• CCFs naturally accommodate multiple outputs, provide a similar computational complexity to random forests, and inherit their impressive robustness to the choice of input parameters.