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Found 7 papers, 1 papers with code
Refinement of Hottopixx Method for Nonnegative Matrix Factorization Under Noisy Separability
In such applications, the robustness of the algorithm to noise is the key to the success.
Improved Analysis of Spectral Algorithm for Clustering
To gain a better understanding of why spectral clustering is successful, Peng et al. (2015) and Kolev and Mehlhorn (2016) studied the behavior of a certain type of spectral algorithm for a class of graphs, called well-clustered graphs.
Convex Programming Based Spectral Clustering
Clustering is a fundamental task in data analysis, and spectral clustering has been recognized as a promising approach to it.
Efficient Preconditioning for Noisy Separable NMFs by Successive Projection Based Low-Rank Approximations
To ensure this, our modification employs a rank-$k$ approximation produced by an SPA based algorithm.
Robustness Analysis of Preconditioned Successive Projection Algorithm for General Form of Separable NMF Problem
However, it may be unrealistic to expect that the condition holds in separable NMF problems appearing in actual applications; in such problems, $d$ is usually greater than $r$.
Spectral Clustering by Ellipsoid and Its Connection to Separable Nonnegative Matrix Factorization
This paper proposes a variant of the normalized cut algorithm for spectral clustering.
Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability
We present a numerical algorithm for nonnegative matrix factorization (NMF) problems under noisy separability.
