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Other studies often implement dimensional reduction techniques in the feature extraction phase. Like the research from Joy and Hasan, which conduct a comparative test of the feature selection method with Minimum Redundancy and Maximum Relevance, feature extraction with PCA, and the PCA-mRMR hybrid model for Image classification with Support Vector Machine. PCA is also used in image feature extraction in gene-culture studies by Wu et al, to analyze the impact on image processing. As a result, feature extraction with PCA significantly improves the quality of training data processing. On the other hand, Al-Ezzi et al, use SVD to extract features in EEG signal data. Extraction is performed in the main components of EEG data to remove background noise and reduce dimensions to improve system performance. Nahiduzzaman et al, proved that SVD can also be combined with the CNN algorithm in a hybrid technique to extract features and classify diabetic retinopathy data. The last, Jadhav et al, consider SVD in the feature extraction process for the segmentation and classification of retinal images. PCA further reduces SVD features before being applied to the k-NN and SVM classification models. This research proves that PCA can also improve the classification performance of machine learning algorithms.
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IEEE/ICACT20230225 Slide.08
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