Engineering Commons^™

Learning Models For Corrupted Multi-Dimensional Data: Fundamental Limits And Algorithms, Ishan Jindal

Wayne State University Dissertations

Developing machine learning models for unstructured multi-dimensional datasets such as datasets with unreliable labels and noisy multi-dimensional signals with or without missing information have becoming a central necessity. We are not always fortunate enough to get noise-free datasets for developing classification and representation models. Though there is a number of techniques available to deal with noisy datasets, these methods do not exploit the multi-dimensional structures of the signals, which could be used to improve the overall classification and representation performance of the model.

In this thesis, we develop a Kronecker-structure (K-S) subspace model that exploits the multi-dimensional structure of the …