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Explore fundamental concepts of multivariate data analysis and chemometrics, including univariate vs. multivariate data, latent variables, and their applications in analytical chemistry.
Automated MATLAB toolbox for spectral preprocessing selection, quantifying noise and scatter effects to optimize PLS models efficiently and objectively, surpassing trial-and-error methods.
Explore tensor decomposition methods beyond sum of squares error, including KL divergence and logistic odds, for improved data analysis in various fields like criminology and neuroscience.
Exploring the black hole effect in Multivariate Curve Resolution, its impact on data analysis, and strategies to overcome this phenomenon in chemometrics and machine learning.
Explore dual-sPLS, an innovative algorithm combining PLS and Lasso for improved variable selection and dimension reduction in high-dimensional analytical chemistry problems, offering enhanced accuracy and interpretability.
Explore computational approaches to enhance LC-MS/MS spectral matching using chemometrics and deep learning for improved molecular structure prediction and compound identification.
Explore data reduction techniques for analyzing large datasets efficiently, focusing on randomized sub-sampling and local-rank approximations in PCA and sparse-PCA contexts.
Explore hybrid multivariate modeling combining theory-driven preprocessing, data-driven chemometrics, and machine learning to extract meaningful information from complex big data measurements.
Explore the foundations of chemometrics with Bruce Kowalski's 1991 lecture, covering key concepts like CLS, PLS, regression, and neural networks that remain relevant in modern data science.
Discover sparse modeling techniques for linear regression, PLS, and PCA with the LASSO method in this concise introduction.
Explore Anova Simultaneous Components Analysis (ASCA), a powerful tool for analyzing multivariate data from designed experiments, in this comprehensive one-hour overview.
Explore simple linear regression using R, learning its powerful applications and detailed implementation in data analysis.
Explore the revamped PARADISe 5.7 beta for enhanced untargeted GCMS data analysis, offering improved information extraction and advanced features for comprehensive analytical insights.
Explore advanced techniques for variable selection in PLS_Toolbox, enhancing your data analysis skills and improving model performance.
Learn techniques for removing baselines and artifacts from data, enhancing signal quality and improving analytical results in chemometrics and machine learning.
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