Semi-supervised Sparse Feature Selection based on Graph Autoencoder by Preservation of Broad and Local Data Structures
Processing and analyzing high-dimensional data is a significant challenge in many domains, and feature selection, as an effective dimension reduction method, plays a key role in improving the performance of machine learning models. Given that in the real world, labeling large volumes of data is cost...
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| Auteurs principaux: | , , |
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| Format: | Artigo |
| Langue: | Persa |
| Publié: |
Yazd University
2025-08-01
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| Collection: | پژوهشهای نظری و کاربردی هوش ماشینی |
| Sujets: | |
| Accès en ligne: | https://abmir.yazd.ac.ir/article_3936_bc1ea08e68ef57ba863b424affb8e47c.pdf |
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