2019
DOI: 10.1007/978-3-030-19738-4_29
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Algorithm of Multidimensional Analysis of Main Features of PCA with Blurry Observation of Facility Features Detection of Carcinoma Cells Multiple Myeloma

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Cited by 14 publications
(11 citation statements)
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“…The Multiple Correspondence Analysis -MCA is an exploratory technique for categorizing variables. [5] [49] For quantitative variables, MCA variants such as the PCA [39], CCPCA [40] [41] and GPCA [42] are used. institutions' and 'Institutions about teachers'.…”
Section: Methodsmentioning
confidence: 99%
“…The Multiple Correspondence Analysis -MCA is an exploratory technique for categorizing variables. [5] [49] For quantitative variables, MCA variants such as the PCA [39], CCPCA [40] [41] and GPCA [42] are used. institutions' and 'Institutions about teachers'.…”
Section: Methodsmentioning
confidence: 99%
“…To predict data, various methods are used, such as linear, logistic and Bayesian regression [35]. To analyze the time series in the situations of many explanatory variables, various selection and extraction methods can be used, which will allow a set of features that best discriminate the explained variable to be obtained.…”
Section: Literature Surveymentioning
confidence: 99%
“…To analyze the time series in the situations of many explanatory variables, various selection and extraction methods can be used, which will allow a set of features that best discriminate the explained variable to be obtained. Such methods include Principal Component Analysis [35,36].…”
Section: Literature Surveymentioning
confidence: 99%
“…In order to determine the similarities between the relationships between the selected morphometric features of the reservoirs, the average temperature of the water retained in them for the duration of ice phenomena occurrence (at a depth of 50 cm), the average thickness of the snow cover deposited on the ice (independent variables) and the average and maximum thickness of the ice (dependent variables) during the three winter seasons, the analysis of the discrimination function, principal components analysis (PCA) and the canonical analysis (RDA) were applied (Jolliffe, 1986;Krzanowski, 2000;Zuur et al, 2007;Topolski and Topolska, 2019;Topolski, 2019Topolski, , 2020a. Redundancy analysis (RDA) is the canonical form of principal component analysis (PCA) and is a method for reducing the dimensionality of multivariate data by introducing a Origin (G, dyke-type; N, subsidence bowl; P g , polygenetic; P e , former extraction pit; S, artificial bowl; Z, dammed-lake).…”
Section: Statisticsmentioning
confidence: 99%
“…To demonstrate the relationship between the groups of independent and dependent variables in the water bodies studied, principal component analysis (PCA) was used. An analysis combining PCA with RDA was applied to determine the detailed directions of the largest differences between the reservoirs and the measured parameters (Topolski, 2019). First, it was checked which dependent and independent variables are most strongly distinguished (discriminated) using discriminant function analysis.…”
Section: Statisticsmentioning
confidence: 99%