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Explore the alternating least squares algorithm for multi-way analysis, focusing on PARAFAC modeling in chemometrics. Learn about notation, potential problems, and alternative approaches.
Explore a novel method for shift-invariant non-negative tensor factorization in GC-MS data analysis, offering faster peak resolution and flexible modeling of elution profile changes.
Explore tensor-based modeling for enhanced classification performance, especially with limited training data, outperforming traditional methods.
Explore TUSCA, a novel method combining Tucker3 and ASCA+ for analyzing multi-way datasets from designed experiments, enhancing interpretation and visualization of complex data structures.
Explore the analytical method development workflow in pharmaceuticals, from ideation to validation, covering key concepts and industry-specific terminology.
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.
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