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Found 5 papers, 2 papers with code
Unrolling PALM for sparse semi-blind source separation
Sparse Blind Source Separation (BSS) has become a well established tool for a wide range of applications - for instance, in astrophysics and remote sensing.
Smoothed Separable Nonnegative Matrix Factorization
More recently, Bhattacharyya and Kannan (ACM-SIAM Symposium on Discrete Algorithms, 2020) proposed an algorithm for learning a latent simplex (ALLS) that relies on the assumption that there is more than one nearby data point to each vertex.
Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures
In this work, we tackle the problem of hyperspectral (HS) unmixing by departing from the usual linear model and focusing on a Linear-Quadratic (LQ) one.
Provably robust blind source separation of linear-quadratic near-separable mixtures
The BF is in turn shown to be robust to noise under easier-to-check and milder conditions than SNPALQ.
Heuristics for Efficient Sparse Blind Source Separation
Sparse Blind Source Separation (sparse BSS) is a key method to analyze multichannel data in fields ranging from medical imaging to astrophysics.
