2025
Classification of High-dimensional Time Series in Spectral Domain Using Explainable Features with Applications to Neuroimaging Data
AISTATS 2025poster
Interpretable classification of time series poses significant challenges in high dimensions. Traditional feature selection methods in the frequency domain often assume sparsity in spectral matrices (or their inverses) which can be restrictive for real-world applications. We propose a model-based app…