ArticleKnowledge-based systems2022
Log-based sparse nonnegative matrix factorization for data representation.
Article in Knowledge-based systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation.Entropy (Basel, Switzerland) · 2025Article
- Fine-Grained Essential Tensor Learning for Robust Multi-View Spectral Clustering.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2024Article
- Global and Local Similarity Learning in Multi-Kernel Space for Nonnegative Matrix Factorization.Knowledge-based systems · 2023Article
- Preserving bilateral view structural information for subspace clustering.Knowledge-based systems · 2022Article
- Hyperspectral Image Denoising Using Nonconvex Local Low-Rank and Sparse Separation With Spatial-Spectral Total Variation Regularization.IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation. However, current NMF methods do not always generate sparse solutions. In this paper, we propose a new NMF method with log-norm imposed on the factor matrices to enhance the sparseness. Moreover, we propose a novel column-wisely sparse norm, named
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