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parafac2 alternating least squares (als) algorithm  (Verlag GmbH)

 
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    Verlag GmbH parafac2 alternating least squares (als) algorithm
    Parafac2 Alternating Least Squares (Als) Algorithm, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/alternating+least+squares+algorithm/parafac2+alternating+least+squares++als++algorithm/pm28025850-44-14-33
    Average 90 stars, based on 1 article reviews
    parafac2 alternating least squares (als) algorithm - by Bioz Stars, 2026-10
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    Article Title: Estimating latent trends in multivariate longitudinal data via Parafac2 with functional and structural constraints.
    Article Snippet: Longitudinal data are inherently multimode in the sense that such data are often collected across multiple modes of variation, for example, time × variables × subjects.. In many longitudinal studies, multiple variables are collected to measure some latent construct(s) of interest.. In such cases, the goal is to understand temporal trends in the latent variables, as well as individual differences in the trends.



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    Non-negative Matrix Factorization of a typical record from a P1 animal. A Grayscale reference image followed by the 13 features required to capture 90% of the variance of the original data, displayed in descending order of relative prominence in sleep vs wake. Note the smaller total number of features required capture the same fraction of variance of the data compared to P6-P8 and the more even weighting of those features between sleep (8) and wake (5), reflecting the more evenly distributed complexity of activity between the two states at P1. Scale bar represents 1mm. B: Time vectors and records as in Fig. 5. C: Relationship between number of features (NMF Rank) and fraction of variance captures for P6-P8 vs P1 animals. On average, fewer features are required to capture equal variances at P1. D-F: Bivariate distributions of Feature Area vs. Sleep Score at P6-P8 (D) and P1 (E), and the Sleep-Wake difference plot (F). Development during the first postnatal week is characterized by added features with wider spread (20–40% area coverage) in sleep (Sleep Score 0.5–1.2). P-values determined using permutation test.

    Journal: Developmental neurobiology

    Article Title: Large-scale waves of activity in the neonatal mouse brain in vivo occur almost exclusively during sleep cycles

    doi: 10.1002/dneu.22901

    Figure Lengend Snippet: Non-negative Matrix Factorization of a typical record from a P1 animal. A Grayscale reference image followed by the 13 features required to capture 90% of the variance of the original data, displayed in descending order of relative prominence in sleep vs wake. Note the smaller total number of features required capture the same fraction of variance of the data compared to P6-P8 and the more even weighting of those features between sleep (8) and wake (5), reflecting the more evenly distributed complexity of activity between the two states at P1. Scale bar represents 1mm. B: Time vectors and records as in Fig. 5. C: Relationship between number of features (NMF Rank) and fraction of variance captures for P6-P8 vs P1 animals. On average, fewer features are required to capture equal variances at P1. D-F: Bivariate distributions of Feature Area vs. Sleep Score at P6-P8 (D) and P1 (E), and the Sleep-Wake difference plot (F). Development during the first postnatal week is characterized by added features with wider spread (20–40% area coverage) in sleep (Sleep Score 0.5–1.2). P-values determined using permutation test.

    Article Snippet: The resulting matrix was factored into a series of paired spatial and temporal components, using the MatLab implementation of the alternating least squares algorithm for NMF.

    Techniques: Activity Assay