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.

Commercializing a disruptive technology based upon university ip through open innovation

This paper discusses the use of a university spin-out firm to bring a potentially disruptive technology to market. The focus for discussion is how a spin-out can build a technology ecosystem of providers of complementary resources to enable partner organizations to build competence in a novel and potentially disruptive technology. The paper uses the illustrative case of Cambridge Display Technology Ltd (CDT) to consider these issues from the perspective of the literature on open innovation (with particular emphasis on the role of partnerships between start-ups and established firms), the commercialization of university IP, and the commercialization of disruptive technologies. © World Scientific Publishing Company.