A methodology for monitoring the upwelling phenomenon using Sentinel-3 products

Karamvasis, Kleanthis; Kolokoussis, Polychronis; Karathanassi, Vassilia; Pasquale, Vito De; Ceriola, Giulio

doi:10.1117/12.2326205

Cited by 1 publication

(2 citation statements)

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“…In this work, we look for relationships between 8 clusters of pixels and 30 clusters for images in different time steps, according to some previous knowledge of the dataset and some estimation criteria [21]. Figure 1 shows the heatmap of the elements involved in the factorization: the original dataset X ∈ R 473981×54 , the block matrix S ∈ R 8×30 and the matrices with information on the clusters of pixels U ∈ R 473981×8 and the clusters of images V ∈ R 30×54 .…”

Section: Bi-clustering With Nmf For Satellite Datamentioning

confidence: 99%

“…The upwelling phenomenon is an upward movement of the water masses due to the interaction between the frictional wind stress on the ocean surface and the rotation of the earth [16]. Different research papers evidenced the difficulties on the study of this problem, presenting satellite data as efficient resources that can significantly help the understanding of the upwelling dynamics using unilateral clustering technique to identify geographical zones particularly relevant for the upwelling activity [17,18]. Nevertheless, when information about different time acquisitions are available, these methodologies are often used exploiting data independently from each other and sometimes clustering operations, working in several direction simultaneously (i.e.…”

Section: Bi-clustering With Nmf For Satellite Datamentioning

confidence: 99%

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Nonnegative Matrix Factorization models for knowledge extraction from biomedical and other real world data

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2021

Proc Appl Math and Mech

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Inspect data for searching valuable information hidden in represents a key aspect in several fields. Fortunately, most of the available data presents an embedded mathematical structure which can be profitably exploited to better investigate latent patterns hidden in them. Dimensionality Reduction (DR) approaches represent one of the most suitable instrument to untangle latent information. These techniques aim to represent data under analysis onto a low-dimensional space allowing to consider most of all of intrinsic knowledge as ideal sources (namely basis) of the process under consideration. In this work we consider Nonnegative Matrix Factorizations (NMFs), which prove to be the most effective among DR approaches in analyzing real-life nonnegative data. NMF simulates the human part-based learning process which states that parts are combined additively to form a whole. Some variants of NMF will be also presented as minimization tasks to which regularization terms can be added in accordance to some additional characteristics (such as sparsity or orthogonality). We investigate significant computational and interpretative aspects related to NMF according to different application domains, with a specific attention to the analysis of biological data. Moreover we present a new NMF model designed for microarray data analysis that incorporates specific biological proprieties as different constraints. Since NMF and its variants are daily used in several application domains, we conclude stressing how NMF and its constrained variants work in some real life applications, showing some original works related to the analysis of data from engineering field.

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Section: Bi-clustering With Nmf For Satellite Datamentioning

confidence: 99%