Abstract: Nonnegative low-rank matrix approximation is an important technique in data analysis for extracting meaningful patterns from high-dimensional nonnegative data. This nonnegative low-rank ...
Low-rank data analysis has emerged as a powerful paradigm across applied mathematics, statistics, and data science. With the rapid growth of modern datasets in size, dimensionality, and complexity, ...
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Is your feature request related to a problem? Please describe. I am currently investigating the numerical evaluation of matrix functions and I noticed that SciPy only ...
Catharina Capitain and Melanie Schüßler from the Faculty of Geosciences at the University of Tübingen, Tübingen, Germany describe a novel approach using matrix-matched semiquantification to ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In this work, we describe the development of a new algorithm for the computation of ...
ABSTRACT: In this work, we seek the relationship between the order of the polynomial model and the number of knots and intervals that we need to fit the splines regression model. Regression models ...
Abstract: Factorizing a low-rank matrix into two matrix factors with low dimensions from its noisy observations is a classical but challenging problem arising from real-world applications. This paper ...
The numerical solution of the radiation transport equation (RTE) is challenging due to the high computational costs and the large memory requirements caused by the high-dimensional phase space. Here ...
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