Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work,  introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show an example of these features in practice,  first present an application to the analysis of climate data.  then present an NMU-based algorithm to robustly fit multiple parametric models to a dataset. The proposed approach delivers state-of-the-art results for the estimation of multiple fundamental matrices and homographies, outperforming other alternatives in the literature and exemplifying the use of efficient NMU computations.
['Mariano Tepper', 'Guillermo Sapiro']
I am writing a research paper on a idea Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting
[{"from": "human", "value": "I am writing a research paper on a idea Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work,  introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show an example of these features in practice,  first present an application to the analysis of climate data.  then present an NMU-based algorithm to robustly fit multiple parametric models to a dataset. The proposed approach delivers state-of-the-art results for the estimation of multiple fundamental matrices and homographies, outperforming other alternatives in the literature and exemplifying the use of efficient NMU computations."}]