About The Speaker

Dr. Ning Ning is an Assistant Professor in the Dept. of Statistics at Texas A&M University. She received her PhD in the Dept. of Statistics and Applied Probability at UCSB. She holds a one year position as Postdoctoral Research Associate in the Dept. of Applied Math at the Univ. of Washington, Seattle and a three year position as Postdoctoral Research Fellow in the Dept. of Statistics at the University of Michigan, Ann Arbor. Her research interest is stochastic processes, Markov chains, time series, networks, and machine learning. She serves as an Associate Editor for Statistics and Computing (Springer).

Event Details

Sparse principal component analysis (SPCA) is a popular tool for dimensionality reduction in high-dimensional data. However, there is still a lack of theoretically justified Bayesian SPCA methods that can scale well computationally. One of the major challenges in Bayesian SPCA is selecting an appropriate prior for the loadings matrix, considering that principal components are mutually orthogonal. We propose a novel parameter-expanded coordinate ascent variational inference (PX-CAVI) algorithm. This algorithm utilizes a spike and slab prior, which incorporates parameter expansion to cope with the orthogonality constraint. Through extensive numerical simulations, we demonstrate that the PX-CAVI algorithm outperforms other SPCA approaches, showcasing its superiority in terms of performance. The R package VBsparsePCA with an implementation of the algorithm is available on CRAN.