The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

Did an extensive forest ever develop on the Chinese Loess Plateau during the past 130 ka?: a test using soil carbon isotopic signatures

IEECAS SKLLQG

Nitrogen-15 signals of leaf-litter-soil continuum as a possible indicator of ecosystem nitrogen saturation by forest succession and N loads

National Key Research and Development Program [2010CB833502]; National Natural Science Foundation of China [30600071, 40601097, 30590381]; Chinese Academy of Sciences [KZCX2-YW-432, O7V70080SZ, LENOM07LS-01]; GUCAS [O85101PM03]

Impact of cloudiness on net ecosystem exchange of carbon dioxide in different types of forest ecosystems in China

Asia 3 Foresight Program [30721140307]; National Key Research and Development Program [2010CB833500]; National Natural Science Foundation of China [30590381, 30900198];

Effects of multiple environmental factors on CO2 emission and CH4 uptake from old-growth forest soils

National Key Research and Development Program [2010CB833502]; National Natural Science Foundation of China [30600071, 40601097, 30590381, 30721140307]; Knowledge Innovation Project of the Chinese Academy of Sciences [KZCX2-YW-432, O7V70080SZ, LENOM07LS-01

Use of Ecosystem Flux Data and a Simulation Model to Examine Seasonal Drought Effects on a Subtropical Coniferous Forest

National Natural Science Foundation of China (NSFC) [30670384, 30590381]

Environmental controls over carbon exchange of three forest ecosystems in eastern China

National Natural Science Foundation of China [30590381, 30670384]; Knowledge Innovation Project of the Chinese Academy of Sciences [KZCX2-YW-432]; National Key Research and Development Program [2002CB412501]; 'Hundred Talents' Program of the Chinese Acade