Date on Master's Thesis/Doctoral Dissertation
5-2016
Document Type
Doctoral Dissertation
Degree Name
Ph. D.
Department
Electrical and Computer Engineering
Degree Program
Electrical Engineering, PhD
Committee Chair
Zurada, Jacek
Committee Co-Chair (if applicable)
El-Baz, Ayman
Committee Member
Inanc, Tamer
Committee Member
Amini, Amir
Committee Member
Nasraoui, Olfa
Author's Keywords
machine learning; deep learning; feature learning; image analysis; segmentation; disease diagnosis
Abstract
The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer's disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction.
Recommended Citation
Hosseini-Asl, Ehsan, "Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis." (2016). Electronic Theses and Dissertations. Paper 2456.
https://doi.org/10.18297/etd/2456
