Date of Award
Master of Arts (AM/MA)
Inspired by classic cocktail-party problem, the basic Independent Component Analysis (ICA) model is created. What differs Independent Component Analysis (ICA) from other kinds of analysis is the intrinsic non-Gaussian assumption of the data. Several approaches are proposed based on maximizing the non-Gaussianity of the data, which is measured by kurtosis, mutual information, and others. With each estimation, we need to optimize the functions of expectations of non-quadratic functions since it can help us to access the higher-order statistics of non-Gaussian part of the data. In this thesis, our goal is to review the one of the most efficient estimation methods, that is, the Fast Fixed-Point Independent Component Analysis (FastICA) algorithm, illustrate it with some examples using an R package.
Chair and Committee
John E. McCarthy
Jose E. Figueroa-Lopez; Jimin Ding
An, Zhenyi, "Different Estimation Methods for the Basic Independent Component Analysis Model" (2018). Arts & Sciences Electronic Theses and Dissertations. 1676.
Permanent URL: https://doi.org/10.7936/0q9c-9854