2016/09/23 by Aaron J. Molstad, Molstad, Aaron J., Adam J. Rothman +1 · 1 citation
Computer Science · Neuroscience · #Blind Source Separation Techniques #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neural Networks and Applications #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.1609.07386
openalex publication_date 2016/09/23 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
We propose a penalized likelihood method to fit the linear discriminant analysis model when the predictor is matrix valued. We simultaneously estimate the means and the precision matrix, which we assume has a Kronecker product decomposition. Our penalties encourage pairs of response category mean matrices to have equal entries and also encourage zeros in the precision matrix. To compute our estimators, we use a blockwise coordinate descent algorithm. To update the optimization variables corresponding to response category mean matrices, we use an alternating minimization algorithm that takes advantage of the Kronecker structure of the precision matrix. We show that our method can outperform relevant competitors in classification, even when our modeling assumptions are violated. We analyze an EEG dataset to demonstrate our method's interpretability and classification accuracy.