Author's School

School of Engineering & Applied Science

Author's Department/Program

Electrical and Systems Engineering


English (en)

Date of Award

Summer 9-1-2014

Degree Type


Degree Name

Doctor of Philosophy (PhD)

Chair and Committee

Arye Nehorai


"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.


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