Abstract
Chapters 4 and 5 have discussed the use of decision forests in supervised tasks, i.e. when labeled training data are available. In contrast, this chapter discusses the use of forests in unlabeled scenarios. For instance, one important task is that of discovering the intrinsic nature and structure of large sets of unlabeled data. This task can be tackled via another probabilistic model, the density forest. Density forests are explained here as an instantiation of our abstract decision forest model as described in Chap. 3.
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Notes
- 1.
Better alternatives, perhaps incorporating priors, may be employed here.
- 2.
A split function is applied usually only to a small, selected subset of features ϕ(v) and thus it can be computed efficiently, i.e. B is very small.
- 3.
No use is made of the ground truth density in this stage.
- 4.
See “consistent estimator” in Wikipedia for a definition of consistency.
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Criminisi, A., Shotton, J. (2013). Density Forests. In: Criminisi, A., Shotton, J. (eds) Decision Forests for Computer Vision and Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4929-3_6
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