Wang, Xiaoqian
(2019)
New Nonlinear Machine Learning Algorithms With Applications to Biomedical Data Science.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Recent advances in machine learning have spawned innovation and prosperity in various fields. In machine learning models, nonlinearity facilitates more flexibility and ability to better fit the data. However, the improved model flexibility is often accompanied by challenges such as overfitting, higher computational complexity, and less interpretability. Thus, it is an important problem of how to design new feasible nonlinear machine learning models to address the above different challenges posed by various data scales, and bringing new discoveries in both theory and applications. In this thesis, we propose several newly designed nonlinear machine learning algorithms, such as additive models and deep learning methods, to address these challenges and validate the new models via the emerging biomedical applications.
First, we introduce new interpretable additive models for regression and classification and address the overfitting problem of nonlinear models in small and medium scale data. we derive the model convergence rate under mild conditions in the hypothesis space and uncover new potential biomarkers in Alzheimer's disease study. Second, we propose a deep generative adversarial network to analyze the temporal correlation structure in longitudinal data and achieve state-of-the-art performance in Alzheimer's early diagnosis. Meanwhile, we design a new interpretable neural network model to improve the interpretability of the results of deep learning methods. Further, to tackle the insufficient labeled data in large-scale data analysis, we design a novel semi-supervised deep learning model and validate the performance in the application of gene expression inference.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
11 September 2019 |
| Date Type: |
Publication |
| Defense Date: |
31 May 2019 |
| Approval Date: |
11 September 2019 |
| Submission Date: |
3 June 2019 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
135 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical and Computer Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
machine learning, nonlinear methods, overfitting, interpretability, Alzheimer's disease |
| Date Deposited: |
11 Sep 2019 14:04 |
| Last Modified: |
11 Sep 2019 14:04 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/36825 |
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