Author: Saban Ozturk; Umut Ozkaya; Mucahid Barstugan
Title: Classification of Coronavirus Images using Shrunken Features Document date: 2020_4_6
ID: 2l1zw19o_37
Snippet: The main purpose of Principle Component Analysis (PCA) is to extract the most significant features from the available data. In this way, it ensures that many feature variables are reduced without any loss of information. PCA takes its place in the literature as a linear analysis method. A different coordinate system occurs by rotating the linear combinations of p randomly distributed data (x1, x2,…, xp) around the original axis. The axes in the.....
Document: The main purpose of Principle Component Analysis (PCA) is to extract the most significant features from the available data. In this way, it ensures that many feature variables are reduced without any loss of information. PCA takes its place in the literature as a linear analysis method. A different coordinate system occurs by rotating the linear combinations of p randomly distributed data (x1, x2,…, xp) around the original axis. The axes in the new coordinate system show the directions of the highest variability. The primary purpose of performing this coordinate conversion is to provide a better interpretation of the data. In cases where the correlations are quite evident in feature reduction, different spinning techniques may show similar results [26] . Obtained features after the rotation are more meaningful.
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