2016
DOI: 10.17562/pb-53-3
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Data Reduction and Regression Using Principal Component Analysis in Qualitative Spatial Reasoning and Health Informatics

Abstract: Abstract-The central idea of principal component analysis (PCA) is to reduce the dimensionality of a dataset consisting of a large number of interrelated variables, while retaining as much as possible of the variation present in the dataset. In this paper, we use PCA based algorithms in two diverse genres, qualitative spatial reasoning (QSR) to achieve lossless data reduction and health informatics to achieve data reduction along with improved regression analysis respectively. In an adaptive hybrid approach, w… Show more

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Cited by 11 publications
(4 citation statements)
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“…The characteristics of Fm, A/Ci, and ostiolar opening were removed from the PCA, considering factor scores < 0.20, in both axes, indicating low representativeness. The use of PCA allows observation of qualitative standards without loss of data information from the difference and similarities represented by two components (dimensions), denoted as PC 1 and PC 2 (Sabharwal & Anjum, 2016). From this multivariate analysis it was identified which characteristics evaluated presented the highest constitution weight in each dimension (axis), considering the loads of factor scores.…”
Section: Resultsmentioning
confidence: 99%
“…The characteristics of Fm, A/Ci, and ostiolar opening were removed from the PCA, considering factor scores < 0.20, in both axes, indicating low representativeness. The use of PCA allows observation of qualitative standards without loss of data information from the difference and similarities represented by two components (dimensions), denoted as PC 1 and PC 2 (Sabharwal & Anjum, 2016). From this multivariate analysis it was identified which characteristics evaluated presented the highest constitution weight in each dimension (axis), considering the loads of factor scores.…”
Section: Resultsmentioning
confidence: 99%
“…67,68 The SVD is related to the principal component analysis. 69,70 The measurements for the comparisons are corrected for delays in connectors, as well as simulated corrections based on measured cable parameters, in order to transform the data into equivalent 50% triggered TIC time delays. One can obtain root-mean-square deviations (RMSD) for each method from a global SVD fit over the 1.5-to 25-m cable range as is shown in Table 2.…”
Section: Delay Measurementmentioning
confidence: 99%
“…The advantage of a principal component analysis (PCA) is that it is able to reduce dimensionality and still preserve most of the variation in data [77]. Thus, the PCA is applied to the household survey dataset to consolidate the different dimensions of flood impacts with financial implications into one score.…”
Section: Principal Component Analysismentioning
confidence: 99%