Discriminative Features via Generalized Eigenvectors

Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking advantage of simple second order structure in the data. We focus on multiclass classification and show that features extracted from the generalized eigenvectors of the class conditional second moments lead to classifiers with excellent empirical performance. Moreover, these features have attractive theoretical properties, such as inducing representations that are invariant to linear transformations of the input. We evaluate classifiers built from these features on three different tasks, obtaining state of the art results

U.S. Private On-orbit Space Situational Awareness Systems and Services: Legal and Regulatory Challenges

One component of Space Traffic Management (STM) is on-orbit Space Situational Awareness (SSA) systems and services. Advances in technology and a growing demand for SSA services, information, and data, coupled with U.S. Government policy that promotes the purchase and use of commercial SSA capabilities, means that private commercial sector is likely to have an important role to play. To date, there is no federal agency with clear jurisdiction over on-orbit remote sensing operations. This paper examines the current regulatory framework, identifies gaps and limitations, and identifies possible ways forward