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Unsupervised feature selection based on the two-dimensional principal component analysis and bipartite graph for face image classification | ||
| Journal of Mahani Mathematical Research | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 13 بهمن 1403 اصل مقاله (3.16 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22103/jmmr.2025.23505.1659 | ||
| نویسندگان | ||
| Firoozeh Beiranvand1؛ Vahid Mehrdad* 1؛ Mohammad Bagher Dowlatshahi2 | ||
| 1Department of Electrical Engineering, Lorestan University, Khorramabad, Iran. | ||
| 2Department of Computer Engineering, Lorestan University, Khorramabad, Iran. | ||
| چکیده | ||
| In this paper, we propose a new matrix-based feature selection method, called UFS2DPCA, which leverages the hidden knowledge in orthogonal features obtained from two-dimensional principal component analysis (2DPCA) to perform accurate unsupervised feature selection. The UFS2DPCA algorithm first uses 2DPCA to directly extract uncorrelated and orthogonal features from the two-dimensional image datasets. We then compute the correlation similarity between the main and extracted features. Finally, a weighted bipartite graph is constructed using two sets of features, and the best features are selected using the fast LAPJV algorithm. The selected features are classified using the K-Nearest Neighbor (KNN) classifier. To ensure statistical significance, the Friedman test is applied to compare the performance of UFS2DPCA with other methods. The algorithm is evaluated on four well-known image datasets: Jaffe, Yale, ORL, and pixraw10P. Key performance metrics such as accuracy, normalized mutual information (NMI), precision, recall, and F-measure are used for evaluation. The experimental results show that UFS2DPCA consistently outperforms other state-of-the-art unsupervised feature selection methods. For example, UFS2DPCA achieves an average NMI of 0.9244 and average accuracy of 0.9033 on the pixraw10P face image dataset that has 10000 features. Similarly, it demonstrates superior performance in accuracy, recall, Precision, F-measure, and NMI across all datasets. | ||
| کلیدواژه‌ها | ||
| Two-Dimensional Principal Component Analysis؛ Weighted Bipartite Graph Matching؛ LAPJV algorithm؛ Augmenting Path؛ similarity | ||
| مراجع | ||
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