Self Modeling Curve Resolution

By W. H. Lawton, E. A. Sylvestre. 1971

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Citations in the corpus

Group invariance principles for causal generative models (2017, methodology): “olds between latent causes and the mechanism. We apply this strategy to speciﬁc unsupervised learning algorithms that have been used in a wide range of areas: Non-negative Matrix factorization (NMF) [Lawton and Sylvestre, 1971; Paatero and Tapper, 1994; Lee and Seung, 1999] and the classical Gaussia”
On rotational ambiguity in model‐free analyses of multivariate data (2006, background): “Rotational ambiguity has been known and documented since the very beginning of chemometrics [20].”
On the methods for reactivation and replay analysis (2020, methodology): “Template free methods seqNMF A recent success in template-free examination of neural sequences was the application of nonnegative matrix factorization (Lawton and Sylvestre, 1971; Paatero and Tapper, 1994) to neural population data (Mackevicius et al.”
Multiset Data Analysis: Extended Multivariate Curve Resolution (2020, background): “This type of constraint contributes significantly to the elimination of rotation ambiguities, because of the presence of zero values in factor Ck and Sl T matrices, which allows in many circumstances for the achievement of unique resolution conditions, as stated by Rolf Manne’s resolution theorems(1”
Natural duality in minimal constrained self modeling curve resolution (2006, methodology): “Self modeling curve resolution (SMCR), one of the oldest chemometric procedure, was introduced for two-component systems by Lawton and Sylvestre (LS) [6] in 1971 to decompose raw spectroscopic data into product of two”
Calculation of maximum and minimum band boundaries of feasible solutions for species profiles obtained by multivariate curve resolution (2001, background): “Non-negativity constraints are probably the most commonly used constraints in curve resolution since the initial work of Lawton and Sylvestre [1].”
Studies on the adaptability of different Borgen norms applied in self‐modeling curve resolution (SMCR) method (2009, methodology): “Lawton and Sylvestre [2] developed the first algorithm for curve resolution originally applied to binary mixtures using the only constraint of nonnegativity of both spectral and concentration values.”
Functional Data Analysis (2016, background): “…data included dynamic time warping (Sakoe & Chiba 1978; Wang & Gasser 1997) for the registration of speech and self-modeling nonlinear regression (Lawton & Sylvestre 1971; Kneip & Gasser 1988), where in the simplest case one assumes that the observed random functions can be modeled as…”