نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشگاه تبریز
2 دانشکده مهندسی برق و کامپیوتر - دانشگاه تبریز
3 استادیار-دانشکده مهندسی برق و کامپیوتر- دانشگاه تبریز
4 دانشکده فناوری اطلاعات و مهندسی کامپیوتر دانشگاه صنعتی ارومیه، ارومیه
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Recently, the use of deep learning for data clustering has gained significant attention due to its ability to uncover complex structures within data. In this paper, a novel ResNet-based autoencoder framework for semi-supervised clustering is proposed. This framework utilizes an autoencoder architecture with residual connections, consisting of two main components: a ResNet-based encoder that extracts meaningful latent representations from the data, and a decoder that is responsible for accurately reconstructing the input data. The proposed model employs a composite loss function that integrates Mean Squared Error (MSE), Kullback-Leibler Divergence (KLD), semi-supervised pairwise constraints, and label-based loss. This innovative combination guides the clustering process using a target distribution as soft labels, ensuring the stability of the model. Experimental results on benchmark datasets demonstrate that the proposed model achieves an average clustering accuracy of 96.8% and 92.5% in Normalized Mutual Information (NMI). These results indicate a significant improvement in performance compared to existing methods in semi-supervised clustering.
کلیدواژه‌ها [English]
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