Certain Characteristics Of Data Research Articles

Data and the Elasticity of Sovereignty

Traditionally, the world map and territorially bounded spaces have dominated ways in which we imagine how states govern, make laws, and exercise their authority. Under this conception, underlined by traditional international law principles of territorial sovereignty, each state would have exclusive authority to govern and make laws over everything concerning the land within its borders. Yet developments like the proliferation of data flows, which are based on divisible, mobile, and interconnected components of data, are not territorially bounded. This presents a challenge to traditional bases for territorial sovereignty and jurisdiction under international law, which some scholars claim is exceptional due to certain characteristics of data. The question as to how states exercise jurisdiction over, or spatially locate, a protean thing such as data, is one which deserves further exploration. By comparing the approaches of China, EU, and the US, this paper explores how laws and regulations in this area are having extraterritorial effects, thus moving away from a strict territorial approach, yet at the same time, are “reterritorializing” by linking their laws on data privacy and data transfers to data infrastructure, data subjects, or data controllers. This shows that states tend to repurpose their territorial sovereignty as they see fit, but this repurposing is not something which is unique to the data governance context. States have used a variety of justifications historically for the extraterritorial exercise of jurisdiction, as well as found creative ways to reterritorialize things, people, events, and processes that were not strictly territorially bounded within their borders. Thus, rather than treat regulations on data privacy and data transfers as an exceptional exercise of territorial sovereignty beyond borders, we might consider this an exemplification of yet another creative reimagining of the elasticity of sovereignty. These exercises in reimagining are significant when we consider their distributional effects and how they allocate decision-making power over governance of data. The elasticity of sovereignty means that authority, governance, and decision-making are ultimately functions of power.

Dimensionality reduction for data of unknown cluster structure

Dimensionality reduction that preserves certain characteristics of data is needed for numerous reasons. In this work we focus on data coming from a mixture of Gaussian distributions and we propose a method that preserves the distinctness of the clustering structure, although this structure is assumed to be yet unknown. The rationale behind the method is the following: (i) had one known the clusters (classes) within the data, one could facilitate further analysis and reduce space dimensionality by projecting the data to the Fisher’s linear subspace, which — by definition — best preserves the structure of the given classes; (ii) under some reasonable assumptions, this can be done, albeit approximately, without prior knowledge of the clusters (classes). In this paper, we show how this approach works. We present a method of preliminary data transformation that brings the directions of largest overall variability close to the directions of the best between-class separation. Hence, for the transformed data, simple PCA provides an approximation to the Fisher’s subspace. We show that the transformation preserves the distinctness of the unknown structure in the data to a great extent.