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Discriminant Projection Forest results, datasets, etc.

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- Reference to latest manuscript - List of possible optional arguments and default values - Tutorials

1. fig 1 of ROFLMAO 2. fig 2 of ROFLMAO 2. fig 3 of ROFLMAO 2. fig 4 of ROFLMAO 4. Lhat(RF) - Lhat(X) distribution figure 5. Lhat vs. treesize...

- http://amstat.tandfonline.com/doi/abs/10.1198/106186006X94072 - http://www.nature.com/modpathol/journal/v18/n4/abs/3800322a.html - http://www.sciencedirect.com/science/article/pii/S1053811912009834 - http://www.cv-foundation.org/openaccess/content_iccv_2013/html/Pei_Unsupervised_Random_Forest_2013_ICCV_paper.html - https://arxiv.org/pdf/1507.04060.pdf - http://www.sciencedirect.com/science/article/pii/S007668790611023X - http://www.sciencedirect.com/science/article/pii/S0888754312000626 - http://mi.eng.cam.ac.uk/~bdrs2/papers/perbet_bmvc09.pdf - http://ieeexplore.ieee.org/document/4603103/ - http://www.sciencedirect.com/science/article/pii/S0967066110001073 - http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2007_133.pdf - http://papers.nips.cc/paper/3191-random-projections-for-manifold-learning - http://dl.acm.org/citation.cfm?id=1374452 - http://ieeexplore.ieee.org/abstract/document/6130407/ @ttomita @mrae...

Tested for sparse and dense sampling on Trunk, Parity, and Multimodal. I didn't see a noticeable difference in performance compared to just sampling -1s and 1s for any dataset or...

just like RF methods, and get dramatic computational improvements for kernel methods. worth reading. [Dai15a.pdf](https://github.com/ttomita/RandomerForest/files/455046/Dai15a.pdf)