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Found 6 papers, 3 papers with code
Causal Fourier Analysis on Directed Acyclic Graphs and Posets
Specifically, we model the spread of an infection on such a DAG obtained from real-world contact tracing data and learn the infection signal from samples assuming sparsity in the Fourier domain.
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases
Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function that is sparse in the Fourier domain.
Digraph Signal Processing with Generalized Boundary Conditions
Furthermore, the Fourier transform in this case is now obtained from the Jordan decomposition, which may not be computable at all for large graphs.
Dynamical Component Analysis (DyCA) and its application on epileptic EEG
Dynamical Component Analysis (DyCA) is a recently-proposed method to detect projection vectors to reduce the dimensionality of multi-variate deterministic datasets.
FFT and orthogonal discrete transform on weight lattices of semi-simple Lie groups
The Gauss-Jordan procedure for the derivation of orthogonal transforms is extended to the multivariate setting.
Numerical Analysis Information Theory Signal Processing Information Theory Representation Theory 65T50, 15A23, 33F99, 68R01, 16G99
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series
Multivariate signal processing is often based on dimensionality reduction techniques.
