Matrix factorization for bilinear blind source separation: Methods, separability and conditioning
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Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/7362053/7362087/07362714.pdf?arnumber=7362714
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Hyperspectral Unmixing Based on Constrained Bilinear or Linear-Quadratic Matrix Factorization;Remote Sensing;2021-05-28
2. Blind source separation methods based on output nonlinear correlation for bilinear mixtures of an arbitrary number of possibly correlated signals;2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM);2020-06
3. Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data;Remote Sensing;2019-09-17
4. From separability/identifiability properties of bilinear and linear-quadratic mixture matrix factorization to factorization algorithms;Digital Signal Processing;2019-04
5. A Blind Source Separation Method Based on Output Nonlinear Correlation for Bilinear Mixtures;Latent Variable Analysis and Signal Separation;2018
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