mlsanityData to 7 Oct 2026

Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation

arXiv 1310.7529 ↗First posted 28 Oct 2013stat.ML cs.LG cs.NAcited by 18

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Abstract (arXiv)

In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive projection algorithm (SPA), but takes advantage of the nonnegativity constraint in the decomposition. We prove that SNPA is more robust than SPA and can be applied to a broader class of nonnegative matrices. This is illustrated on some synthetic data sets, and on a real-world hyperspectral image.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2013.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 5; 2025: 4; 2026: 2.

18 papers.

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  1. 2022Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixingcited by 6
  2. 2021Smoothed Separable Nonnegative Matrix Factorizationcited by 5
  3. 2024Dual Simplex Volume Maximization for Simplex-Structured Matrix Factorizationcited by 4
  4. 2021Refinement of Hottopixx Method for Nonnegative Matrix Factorization Under Noisy Separabilitycited by 3
  5. 2022A consistent and flexible framework for deep matrix factorizationscited by 3
  6. 2024On the Robustness of the Successive Projection Algorithmcited by 3
  7. 2024Endmember Extraction from Hyperspectral Images Using Self-Dictionary Approach with Linear Programmingcited by 2
  8. 2021Co-Separable Nonnegative Matrix Factorizationcited by 1
  9. 2023Towards Tuning-Free Minimum-Volume Nonnegative Matrix Factorizationcited by 1
  10. 2024Coseparable Nonnegative Tensor Factorization With T-CUR Decompositioncited by 1
  11. 2025Rethinking Coupled Tensor Analysis for Hyperspectral Super-Resolution: Recoverable Modeling Under Endmember Variabilitycited by 1
  12. 2025Nonnegative matrix factorizations and related compositional models: Equivalence, identifiability, and an application on the grain-size analysis of sedimentscited by 1
6 more
  1. 2026Identification of NMF by choosing maximum-volume basis vectorscited by 1
  2. 2021fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clusteringcited by 0
  3. 2024Orthogonal Nonnegative Matrix Factorization with the Kullback-Leibler divergencecited by 0
  4. 2025Hyperspectral Image Data Reduction for Endmember Extractioncited by 0
  5. 2025Automating Historical Insight Extraction from Large-Scale Newspaper Archives via Neural Topic Modelingcited by 0
  6. 2026Super-r'esolution non supervis'ee d'images hyperspectrales de t'el'ed'etection utilisant un entrainement enti`erement synth'etiquecited by 0