Autoencoder-PCA-based Online Supervised Feature Extraction-Selection Approach | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 4، بهمن 2023، صفحه 525-534 اصل مقاله (1.8 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2023.12436.2390 | ||
| نویسندگان | ||
| Amir Mehrabinezhad1؛ Mohammad Teshnelab* 2؛ Arash Sharifi1 | ||
| 1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. | ||
| 2Faculty of Electronic and Computer Engineering Department, K.N Toosi University of Technology, Tehran, Iran. | ||
| چکیده | ||
| Due to the growing number of data-driven approaches, especially in artificial intelligence and machine learning, extracting appropriate information from the gathered data with the best performance is a remarkable challenge. The other important aspect of this issue is storage costs. The principal component analysis (PCA) and autoencoders (AEs) are samples of the typical feature extraction methods in data science and machine learning that are widely used in various approaches. The current work integrates the advantages of AEs and PCA for presenting an online supervised feature extraction selection method. Accordingly, the desired labels for the final model are involved in the feature extraction procedure and embedded in the PCA method as well. Also, stacking the nonlinear autoencoder layers with the PCA algorithm eliminated the kernel selection of the traditional kernel PCA methods. Besides the performance improvement proved by the experimental results, the main advantage of the proposed method is that, in contrast with the traditional PCA approaches, the model has no requirement for all samples to feature extraction. As regards the previous works, the proposed method can outperform the other state-of-the-art ones in terms of accuracy and authenticity for feature extraction. | ||
| کلیدواژهها | ||
| principal component analysis (PCA)؛ online PCA؛ autoencoder؛ stacked autoencoder؛ semi-supervised learning | ||
| مراجع | ||
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