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- W3004339322 abstract "The work is motivated by problems of mathematical supply of distributed marine monitoring systems and is devoted to dimension reducing algorithms aimed at early stage discovering of wrecks. Technologies to detect vector process dissensions at early stage using singular value decomposition (SVD) of data matrix are proposed. These technologies are based on the C. Eckart and G. Young result that SVD solves the problem of low rank matrix approximation. A variant of that approach is also referred to as immunocomputing and is regarded at the neurobiological area of artificial intellect. By now similar techniques finds applications at different problems of revealing irregular situations, image recognition, data compression. The principal advantage here is that the approach under regard permits to solve problems of purposeful dimensionality reduction of multivariate data directly by data matrix without estimating of covariances. It’s most valuable at data of high dynamics when current conclusions have to be based on sliding windows of comparatively moderate volume. In application to problems of multichannel data fusion it is essential that this approach doesn’t use the idea of centering with respect to the mean value and makes it possible to expand the traditional model of the class (situation) as a realization of n Gaussian vectors with common mean regarded as an “ideal representative of the class of interest”. The approach may also be regarded as an analog of factor analysis based on alternate description of dispersion characteristics." @default.
- W3004339322 created "2020-02-07" @default.
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- W3004339322 date "2020-01-01" @default.
- W3004339322 modified "2023-10-14" @default.
- W3004339322 title "Elaboration of Multichannel Data Fusion Algorithms at Marine Monitoring Systems" @default.
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- W3004339322 doi "https://doi.org/10.1007/978-3-030-37919-3_90" @default.
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