Author's Department/Program
Electrical and Systems Engineering
Language
English (en)
Date of Award
Summer 9-1-2014
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Chair and Committee
Arye Nehorai
Abstract
"In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications" [151]. In a
world of increasing sensor modalities, cheaper storage, and more data oriented questions, we are quickly passing the limits of tractable computations using traditional statistical analysis
methods. Methods which often show great results on simple data have difficulties processing complicated multidimensional data. Accuracy alone can no longer justify unwarranted memory
use and computational complexity. Improving the scaling properties of these methods for multidimensional data is the only way to make these methods relevant. In this work we explore methods for improving the scaling properties of parametric and nonparametric
models. Namely, we focus on the structure of the data to lower the complexity of a specific family of problems. The two types of structures considered in this work are distributive
optimization with separable constraints (Chapters 2-3), and scaling Gaussian processes for multidimensional lattice input (Chapters 4-5). By improving the scaling of these methods, we can expand their use to a wide range of applications which were previously intractable
open the door to new research questions.
Recommended Citation
Gilboa, Elad, "Scaling Multidimensional Inference for Big Structured Data" (2014). All Theses and Dissertations (ETDs). 1303.
https://openscholarship.wustl.edu/etd/1303
Comments
This work is not available online per the author’s request. For access information, please contact digital@wumail.wustl.edu or visit http://digital.wustl.edu/publish/etd-search.html.
Permanent URL: http://dx.doi.org/10.7936/K7DV1GXV