Date of Award
Level of Access Assigned by Author
Master of Science (MS)
Electrical and Computer Engineering
Mohamad T. Musavi
Second Committee Member
Third Committee Member
Principal Components Analysis (PCA) is a conventional linear technique for projecting multidimensional data onto lower dimensional spaces with minimal loss of variance. However, there are several applications where the data is not linear; in these cases linear PCA is not the optimal method to recover this subspace and thus account for the largest proportion of variance in the data. In this thesis, a non-linear PCA (IVLPCA) method is developed using a new technique that combines Radial Basis function with Particle Swarm optimization. The new technique is evaluated and compared to other standard methods in the applications of function approximation, feature extraction, and process monitoring.
Driss, Mohamed Naoufel, "Non-linear PCA Based on Radial Basis Function and Particle Swarm Optimization" (2005). Electronic Theses and Dissertations. 986.