Dissertation Announcement for Muhammad Aminul Islam
05/30/2018 at 9:00 AM
May 10, 2018
Dear Faculty, Graduate and Undergraduate students,
You are cordially invited to my Ph.D. dissertation defense.
Dissertation Title: Efficient Data Driven Multi Source Fusion.
When: Wednesday, May 30, 2018, 9:00 AM
Where: Simrall 228
Candidate: Muhammad Aminul Islam
Degree: Doctor of Philosophy, Electrical and Computer Engineering
Committee:
Dr. John E. Ball, Assistant Professor of Electrical and Computer Engineering
(Major Professor)
Dr. Derek T. Anderson, Former Associate Professor of Electrical and Computer Engineering at Mississippi State University and Current Associate Professor of Electrical Engineering and Computer Science at University of Missouri
(Co-Major Professor)
Dr. Nicolas H. Younan, Professor of Electrical and Computer Engineering
(Committee Member)
Dr. James E. Fowler, Professor of Electrical and Computer Engineering
(Committee Member)
Abstract
Data/information fusion is an integral component of many existing and emerging applications; e.g., remote sensing, smart cars, Internet of Things (IoT), and Big Data, to name a few. While fusion aims to achieve better results than what any one individual input can provide, often the challenge is to determine the underlying mathematics for aggregation suitable for an application. In this dissertation, I focus on the following three aspects of aggregation: (i) efficient data-driven learning and optimization, (ii) extensions and new aggregation methods, and (iii) feature and decision level fusion for machine learning with applications to signal and image processing.
The Choquet integral (ChI), a powerful nonlinear aggregation operator, is a parametric way (with respect to the fuzzy measure (FM)) to generate a wealth of aggregation operators. The FM has 2^N variables and N(2^N – 1) constraints for N inputs. As a result, learning the ChI parameters from data quickly becomes impractical for most applications. Herein, I propose a scalable learning procedure (which is linear with respect to training sample size) for the ChI that identifies and optimizes only data-supported variables. As such, the computational complexity of the learning algorithm is proportional to the complexity of the solver used. This method also includes an imputation framework to obtain scalar values for data-unsupported (aka missing) variables and a compression algorithm (lossy or losselss) of the learned variables. I also propose a genetic algorithm (GA) to optimize the ChI for non-convex, multi-modal, and/or analytical objective functions. This algorithm introduces two operators that automatically preserve the constraints; therefore there is no need to explicitly enforce the constraints as is required by traditional GA algorithms. In addition, this algorithm provides an efficient representation of the search space with the minimal set of vertices. Furthermore, I study different strategies for extending the fuzzy integral for missing data and I propose a GOAL programming framework to aggregate inputs from heterogeneous sources for the ChI learning. Last, my work in remote sensing involves visual clustering based band group selection and Lp-norm multiple kernel learning based feature level fusion in hyperspectral image processing to enhance pixel level classification.
Regards,
Muhammad Aminul Islam