Semi-Supervised Random Forests

11 years 2 months ago
Semi-Supervised Random Forests
Random Forests (RFs) have become commonplace in many computer vision applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while still being able to achieve state-of-the-art accuracy. This work extends the usage of Random Forests to Semi-Supervised Learning (SSL) problems. We show that traditional decision trees are optimizing multiclass margin maximizing loss functions. From this intuition, we develop a novel multi-class margin definition for the unlabeled data, and an iterative deterministic annealing-style training algorithm maximizing both the multi-class margin of labeled and unlabeled samples. In particular, this allows us to use the predicted labels of the unlabeled data as additional optimization variables. Furthermore, we propose a control mechanism based on the out-of-bag error, which prevents the algorithm from degradation if the unlabeled data is not useful for the task. Our experiments ...
Christian Leistner, Amir Saffari, Jakob Santner, H
Added 13 Jul 2009
Updated 08 Jul 2010
Type Conference
Year 2009
Where ICCV
Authors Christian Leistner, Amir Saffari, Jakob Santner, Horst Bischof
Comments (0)