We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification. Individual canonical correlation trees are binary decision trees with hyperplane splits based on canonical correlation components. Unlike axis-aligned alternatives, the decision surfaces of CCFs are not restricted to the coordinate system of the input features and therefore more naturally represent data with correlation between the features. Additionally we introduce a novel alternative to bagging, the projection bootstrap, which maintains use of the full dataset in selecting split points. CCFs do not require parameter tuning and our experiments show that they significantly out-perform axis-aligned random forests and other state-of-the-art tree ensemble methods.
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