1,075 research outputs found
Local-Aggregate Modeling for Big-Data via Distributed Optimization: Applications to Neuroimaging
Technological advances have led to a proliferation of structured big data
that have matrix-valued covariates. We are specifically motivated to build
predictive models for multi-subject neuroimaging data based on each subject's
brain imaging scans. This is an ultra-high-dimensional problem that consists of
a matrix of covariates (brain locations by time points) for each subject; few
methods currently exist to fit supervised models directly to this tensor data.
We propose a novel modeling and algorithmic strategy to apply generalized
linear models (GLMs) to this massive tensor data in which one set of variables
is associated with locations. Our method begins by fitting GLMs to each
location separately, and then builds an ensemble by blending information across
locations through regularization with what we term an aggregating penalty. Our
so called, Local-Aggregate Model, can be fit in a completely distributed manner
over the locations using an Alternating Direction Method of Multipliers (ADMM)
strategy, and thus greatly reduces the computational burden. Furthermore, we
propose to select the appropriate model through a novel sequence of faster
algorithmic solutions that is similar to regularization paths. We will
demonstrate both the computational and predictive modeling advantages of our
methods via simulations and an EEG classification problem.Comment: 41 pages, 5 figures and 3 table
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
- …