Overcoming dimensionality constraints: a gershgorin circle theorem-based feature extraction for weighted laplacian matrices in computer vision applications

HIGHLIGHTS

• What: Motivated by this issue, this study introduces an innovative approach to mitigate the ‘curse of dimensionality` and low computational time without significantly compromising classification accuracy. This research shows how the performance of these feature extraction algorithms is influenced by variations in image patch sizes. This study compares the proposed method with seven feature reduction methods and one non-feature reduction algorithm with identical CNN classification architecture. In the second approach, additional experiments were conducted to compare the feature extraction algorithms with different graph types, their accuracy, and computational time presented in Table 3.
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