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Everything about Non-negative matrix factorization
Algorithms for matrix decomposition
Non-negative matrix factorization, also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements. This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms or muscular activity, non-negativity is inherent to the data being considered. Since the problem is not exactly solvable in general, it is commonly approximated numerically. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Non-negative matrix factorizationA 5-minute orientation
- Skim a recent paper: Link prediction by deep non-negative matrix factorizationInfluential work since 2022
- Pick up a classic book: Neural Information ProcessingThe most-reprinted book on the subject
- Read the landmark paper: Learning the parts of objects by non-negative matrix factorizationCited 11,771 times
- Go sideways: Principal component analysisA closely related topic
- Go sideways: Feature engineeringA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Learning the parts of objects by non-negative matrix factorization
Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data values
Non-negative matrix factorization with α-divergence
Robust non-negative matrix factorization
Truncated Cauchy Non-Negative Matrix Factorization
Email Surveillance Using Non-negative Matrix Factorization
NON-NEGATIVE MATRIX FACTORIZATION FRAMEWORK FOR FACE RECOGNITION
Non-negative Matrix Factorization: A Survey
🆕 Influential since 2022
Link prediction by deep non-negative matrix factorization
Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering
Multi-view clustering guided by unconstrained non-negative matrix factorization
Robust multi-view non-negative matrix factorization for clustering
Initialization for non-negative matrix factorization: a comprehensive review
📚 Books worth reading
Neural Information ProcessingTingwen Huang · 2012
Nonnegative matrix and tensor factorizationsAndrzej Cichocki · 2009- 📕Applied data miningGuandong Xu · 2013
Non-negative Matrix Factorization TechniquesGanesh R. Naik · 2015
Principles of gene manipulation and genomicsS. B. Primrose · 2006
Independent Component AnalysisAddisson Salazar, Luis Vergara · 2018
Socioeconomic and legal implications of electronic intrusionDionysios Politis · 2009
Brain InformaticsFabio Massimo Zanzotto · 2012
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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