Likelihood Component Analysis

• Activity Number: 481
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Benjamin Risk* and David S. Matteson and David Ruppert
• Companies: Cornell University and Cornell University and Cornell University
• Keywords: independent component analysis ; fMRI ; non-Gaussian component analysis ; principal component analysis ; projection pursuit ; artifact detection

Abstract

Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis is used for dimension reduction prior to ICA (PCA-ICA), which could remove important information. To address this issue, we propose likelihood component analysis (LCA) in which dimension reduction and latent variable estimation is achieved simultaneously by maximizing a likelihood with Gaussian and non-Gaussian components. We present a semi-parametric version using tilted Gaussians with cubic B-splines. We implement an algorithm scalable to datasets common in applications. In simulations, our methods recover latent components that are discarded by PCA-ICA methods. PCA-ICA is a popular technique to identify artifacts in functional magnetic resonance imaging. We apply our method to an experiment from the Human Connectome Project with state-of-the-art temporal and spatial resolution, and identify an artifact using LCA that was missed by PCA-ICA. Our results suggest that likelihood component analysis can detect novel signals in neuroimagery.