Abstract: Robust tensor principal component analysis (RTPCA) based on tensor singular value decomposition (t-SVD) separates the low-rank component and the sparse component from the multiway data. For ...
For Erik Lathen, the principal of Illinois Valley High School in Cave Junction, Ore., gratitude isn’t something to be celebrated only on Thanksgiving. During his nine-year tenure as a high school ...
Background: The high-quality development of the traditional Chinese medicine (TCM) industry is dependent on supportive policies and requires higher levels of coordination and integration. National and ...
ABSTRACT: This article examines the effect of economic vulnerability on inclusive growth across 49 developing countries from 1991 to 2020, focusing on the mitigating role of agricultural structural ...
The authors present a critique of current usage of principal component analysis in geometric morphometrics, making a compelling case with benchmark data that standard techniques perform poorly. The ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Mass spectrometry imaging (MSI) is constantly improving in spatial resolving power, ...
Department of Agrobiotechnology, IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, BOKU University, Konrad-Lorenz-Str. 20, 3430 Tulln an der Donau, Austria Department of Agrobiotechnology, ...
Abstract: Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic ...
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