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Found 5 papers, 5 papers with code
Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images
By unfolding iterative optimization methods into neural networks, this approach offers enhanced interpretability and reliability compared to data-driven deep learning methods, and greater adaptability and generalization than model-based approaches.
OsmLocator: locating overlapping scatter marks with a non-training generative perspective
In addition, we especially built a dataset named SML2023 containing hundreds of scatter images with different markers and various levels of overlapping severity, and tested the proposed method and compared it to existing methods.
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.
Matrix-wise $\ell_0$-constrained Sparse Nonnegative Least Squares
In this paper, as opposed to most previous works that enforce sparsity column- or row-wise, we first introduce a novel formulation for sparse MNNLS, with a matrix-wise sparsity constraint.
Sparse Separable Nonnegative Matrix Factorization
We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions.
