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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Autors principals:	MohammadJavd Reezaei, MahdiAgha Sarram, Razieh Sheikhpour
Format:	Artigo
Idioma:	Persa
Publicat:	Yazd University 2025-08-01
Col·lecció:	پژوهش‌های نظری و کاربردی هوش ماشینی
Matèries:	semi-supervised feature selection auto encoder sparse models l_(2 1)-norm
Accés en línia:	https://abmir.yazd.ac.ir/article_3936_bc1ea08e68ef57ba863b424affb8e47c.pdf
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