%0 Journal Article %1 mello2020lasso %A Mello, Cesar %A Escudeiro, Cassiano %A Noda, Isao %D 2020 %J Applied Mathematics and Sciences: An International Journal (MathSJ) %K NIR biodiesel from lassomodel modeling multivariate recyclable sources. sparsity spectroscopy %N 1 %P 01 - 12 %T LASSO MODELING AS AN ALTERNATIVE TO PCA BASED MULTIVARIATE MODELS TO SYSTEM WITH HEAVY SPARSITY: “BIODIESEL QUALITY BY NIR SPECTROSCOPY” %U https://airccse.com/mathsj/papers/7120mathsj01.pdf %V 7 %X Principal component analysis (PCA) is a widespread and widely used in various areas of science such as bioinformatics, econometrics, and chemometrics among others. Once that PCA is based in the eigenvalues and the eigenvectors which are a very weak approach to high dimension systems with degrees of sparsity and in these situations the PCA is no longer a recommended procedure. Sparsity is very common in near infrared spectroscopy due to the large number of spectra required and the water absorption broad bands what makes these spectra very similar and with heavy sparsity in matrix dataset, demoting the precision and accuracy, in the multivariate modeling and within projections of data matrix in smaller dimensions. To overcoming these shortcomings the LASSO, a not PCA based method, model was applied to a NIR spectra dataset from Biodiesel and its performance was, statistically, compared with traditional multivariate modeling such as PCR and PLSR.

@article{mello2020lasso, abstract = {Principal component analysis (PCA) is a widespread and widely used in various areas of science such as bioinformatics, econometrics, and chemometrics among others. Once that PCA is based in the eigenvalues and the eigenvectors which are a very weak approach to high dimension systems with degrees of sparsity and in these situations the PCA is no longer a recommended procedure. Sparsity is very common in near infrared spectroscopy due to the large number of spectra required and the water absorption broad bands what makes these spectra very similar and with heavy sparsity in matrix dataset, demoting the precision and accuracy, in the multivariate modeling and within projections of data matrix in smaller dimensions. To overcoming these shortcomings the LASSO, a not PCA based method, model was applied to a NIR spectra dataset from Biodiesel and its performance was, statistically, compared with traditional multivariate modeling such as PCR and PLSR.}, added-at = {2021-03-03T06:22:01.000+0100}, author = {Mello, Cesar and Escudeiro, Cassiano and Noda, Isao}, biburl = {https://www.bibsonomy.org/bibtex/2b8c900b61af6a0385f8ca3384b1767bc/journalmathsj}, interhash = {925ee0d1b2c9bfa8dc48c718e067c792}, intrahash = {b8c900b61af6a0385f8ca3384b1767bc}, journal = {Applied Mathematics and Sciences: An International Journal (MathSJ)}, keywords = {NIR biodiesel from lassomodel modeling multivariate recyclable sources. sparsity spectroscopy}, month = {March}, number = 1, pages = {01 - 12}, timestamp = {2021-03-03T06:22:01.000+0100}, title = {LASSO MODELING AS AN ALTERNATIVE TO PCA BASED MULTIVARIATE MODELS TO SYSTEM WITH HEAVY SPARSITY: “BIODIESEL QUALITY BY NIR SPECTROSCOPY”}, url = {https://airccse.com/mathsj/papers/7120mathsj01.pdf}, volume = 7, year = 2020 }