Linear algebra
Branch of mathematics
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| 2024 |
MATLAB
MATLAB releases R2024a (version 24.1) and R2024b (version 24.2) with corresponding Simulink versions 24.1 and 24.2
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| April 23 2024 |
Mathcad
Release of Mathcad Prime 10, the latest version of the mathematical calculation software.
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| 2023 |
MATLAB
MATLAB releases R2023a (version 9.14) and R2023b (version 23.2) with corresponding Simulink versions 10.7 and 23.2
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| 2022 |
MATLAB
MATLAB releases R2022a (version 9.12) and R2022b (version 9.13) with corresponding Simulink versions 10.5 and 10.6
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| 2021 |
MATLAB
MATLAB releases R2021a (version 9.10) and R2021b (version 9.11) with corresponding Simulink versions 10.3 and 10.4
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| 2020 |
Non-negative matrix factorization
Ren et al. published a comprehensive study on Non-negative Matrix Factorization (NMF) for data imputation, focusing on mathematical derivation and application in astronomy, demonstrating a method for handling missing data in two-dimensional matrices.
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| 2020 |
Non-negative matrix factorization
Nicolas Gillis published 'Nonnegative Matrix Factorization' through SIAM, providing a comprehensive academic treatment of the topic.
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| 2020 |
MATLAB
MATLAB withdrew services from two Chinese universities in response to US sanctions, prompting the universities to consider increasing use of open-source alternatives and developing domestic replacements.
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| 2020 |
MATLAB
MATLAB releases R2020a (version 9.8) and R2020b (version 9.9) with corresponding Simulink versions 10.1 and 10.2
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| 2019 |
Non-negative matrix factorization
Hassani, Iranmanesh and Mansouri proposed a feature agglomeration method for term-document matrices using NMF, which reduces the matrix to a smaller, more clustering-friendly format.
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| 2019 |
Non-negative matrix factorization
Shoji Makino edited 'Audio Source Separation', highlighting continued academic interest in NMF applications.
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| 2019 |
MATLAB
MATLAB releases R2019a (version 9.6) and R2019b (version 9.7) with corresponding Simulink versions 9.3 and 10.0
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| 2018 |
Non-negative matrix factorization
Jen-Tzung Chien published 'Source Separation and Machine Learning', contributing to the ongoing research in NMF and related machine learning techniques.
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| 2018 |
Non-negative matrix factorization
Ren et al. adopted NMF to the direct imaging field, using it as a method for detecting exoplanets, particularly for direct imaging of circumstellar disks. They also proved the stability of NMF components when constructed sequentially.
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| 2018 |
MATLAB
MATLAB releases R2018a (version 9.4) and R2018b (version 9.5) with corresponding Simulink versions 9.1 and 9.2
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| 2016 |
Non-negative matrix factorization
Zhu improved the NMF method for spectroscopic observations by incorporating handling of missing data and enabling parallel computing.
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| 2016 |
Non-negative matrix factorization
Two significant publications emerged: Ganesh R. Naik's edited volume 'Non-negative Matrix Factorization Techniques: Advances in Theory and Applications' and Julian Becker's work on 'Nonnegative Matrix Factorization with Adaptive Elements for Monaural Audio Source Separation'.
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| 2014 |
Non-negative matrix factorization
Yong Xiang published 'Blind Source Separation: Dependent Component Analysis', further exploring techniques related to non-negative matrix factorization.
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| 2013 |
Non-negative matrix factorization
Arora, Ge, Halpern, Mimno, Moitra, Sontag, Wu, & Zhu proposed polynomial-time algorithms for learning topic models using Non-negative Matrix Factorization (NMF), introducing an algorithm that assumes topics satisfy a separability condition.
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| 2011 |
Non-negative matrix factorization
Andri Mirzal published 'Nonnegative Matrix Factorizations for Clustering and LSI: Theory and Programming', providing insights into NMF applications and computational approaches.
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This contents of the box above is based on material from the Wikipedia articles Non-negative matrix factorization, Mathcad, MATLAB & K-SVD, which are released under the Creative Commons Attribution-ShareAlike 4.0 International License.