mlsanityData to 7 Oct 2026

Initialization for Nonnegative Matrix Factorization: a Comprehensive Review

arXiv 2109.03874 ↗First posted 8 Sep 2021math.OC cs.LGcited by 4

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Abstract (arXiv)

Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix. Some of the unique features of this method in identifying hidden data put this method amongst the powerful methods in the machine learning area. The NMF is a known non-convex optimization problem and the initial point has a significant effect on finding an efficient local solution. In this paper, we investigate the most popular initialization procedures proposed for NMF so far. We describe each method and present some of their advantages and disadvantages. Finally, some numerical results to illustrate the performance of each algorithm are presented.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 102 references that are in the map. First posted on arXiv 8 Sep 2021.

Builds on

8 of 102 references.

References · 8

  1. 2021Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-viewcited by 30
  2. 2014Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorizationcited by 9
  3. 2015Compressed Nonnegative Matrix Factorization is Fast and Accuratecited by 4
  4. 2019Nonnegative Matrix Factorization with Local Similarity Learningcited by 3
  5. 2020Two-Dimensional Semi-Nonnegative Matrix Factorization for Clusteringcited by 3
  6. 2018Improved SVD-based Initialization for Nonnegative Matrix Factorization using Low-Rank Correctioncited by 2
  7. 2013Matrix Approximation under Local Low-Rank Assumptioncited by 1
  8. 2016Rank-One NMF-Based Initialization for NMF and Relative Error Bounds under a Geometric Assumptioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 2.

4 papers.

  1. 2023A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimationcited by 108
  2. 2024GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorizationcited by 1
  3. 2026An Exterior Method for Nonnegative Matrix Factorizationcited by 1
  4. 2026Sparse Network Inference under Imperfect Detection and its Application to Ecological Networkscited by 0