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Nonlinear Semi-supervised and Unsupervised Metric Learning with Applications in Neuroimaging

Zhang, Pin
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1525266545968548

Abstract Details

2018, Doctor of Philosophy (PhD), Ohio University, Electrical Engineering & Computer Science (Engineering and Technology).
In many machine learning and data mining algorithms, pairwise distances (or dissimilarities) among data samples are computed based on the Euclidean metric, where all feature components are treated equally and assigned with the same weight. Learning a customized metric from the input data can often significantly improve the performance of the algorithms. In this dissertation, we propose two nonlinear distance metric learning (DML) frameworks to boost the performance of semi-supervised learning (SSL) and unsupervised learning (USL) algorithms, respectively. Formulated under a constrained optimization framework, our proposed SSL-DML method learns a smooth nonlinear feature space transformation that makes the input data samples more linearly separable in Laplacian SVM (LapSVM). Our USL-ML solution, on the other hand, aims to increase data's linear separability for k-means. A geometric model called Coherent Point Drifting (CPD) is utilized in both frameworks to move data points towards more desirable locations. The choice of CPD is with two considerations: 1) its remarkable capability in generating high-order yet smooth deformations; and 2) the available mechanism within CPD for assigning different levels of smoothness to data points. Application-wise, we apply our SSL-DML to predict the conversion of Alzheimer's Disease (AD) from its early stage: Mild Cognitive Impairment (MCI). The proposed USL-DML solution is utilized to improve the patient clustering. Using neuroimage data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we evaluate the effectiveness of the proposed frameworks. The experimental results demonstrate the improvements over the state-of-the-art solutions within the same category.
Jundong Liu (Advisor)
118 p.
Computer Science; Electrical Engineering
Nonlinear; Semi-supervised; Unsupervised; Metric Learning; Neuroimaging; Alzheimers; AD; Coherent Point Drifting; CPD; Geometric Transformation; Mild Cognitive Impairment; MCI; Alzheimers Disease Neuroimaging Initiative; ADNI; Machine Learning; cluster

Recommended Citations

Citations

  • Zhang, P. (2018). Nonlinear Semi-supervised and Unsupervised Metric Learning with Applications in Neuroimaging [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1525266545968548

    APA Style (7th edition)

  • Zhang, Pin. Nonlinear Semi-supervised and Unsupervised Metric Learning with Applications in Neuroimaging. 2018. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1525266545968548.

    MLA Style (8th edition)

  • Zhang, Pin. "Nonlinear Semi-supervised and Unsupervised Metric Learning with Applications in Neuroimaging." Doctoral dissertation, Ohio University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1525266545968548

    Chicago Manual of Style (17th edition)

ohiou1525266545968548
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© 2018, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.