{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:19:23Z","timestamp":1781659163899,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61876189"],"award-info":[{"award-number":["61876189"]}]},{"name":"National Natural Science Foundation of China","award":["61273275"],"award-info":[{"award-number":["61273275"]}]},{"name":"National Natural Science Foundation of China","award":["61806219"],"award-info":[{"award-number":["61806219"]}]},{"name":"National Natural Science Foundation of China","award":["61703426"],"award-info":[{"award-number":["61703426"]}]},{"name":"National Natural Science Foundation of China","award":["2021JM\u2014226"],"award-info":[{"award-number":["2021JM\u2014226"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province","award":["61876189"],"award-info":[{"award-number":["61876189"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province","award":["61273275"],"award-info":[{"award-number":["61273275"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province","award":["61806219"],"award-info":[{"award-number":["61806219"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province","award":["61703426"],"award-info":[{"award-number":["61703426"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province","award":["2021JM\u2014226"],"award-info":[{"award-number":["2021JM\u2014226"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The efficiency and cognitive limitations of manual sample labeling result in a large number of unlabeled training samples in practical applications. Making full use of both labeled and unlabeled samples is the key to solving the semi-supervised problem. However, as a supervised algorithm, the stacked autoencoder (SAE) only considers labeled samples and is difficult to apply to semi-supervised problems. Thus, by introducing the pseudo-labeling method into the SAE, a novel pseudo label-based semi-supervised stacked autoencoder (PL-SSAE) is proposed to address the semi-supervised classification tasks. The PL-SSAE first utilizes the unsupervised pre-training on all samples by the autoencoder (AE) to initialize the network parameters. Then, by the iterative fine-tuning of the network parameters based on the labeled samples, the unlabeled samples are identified, and their pseudo labels are generated. Finally, the pseudo-labeled samples are used to construct the regularization term and fine-tune the network parameters to complete the training of the PL-SSAE. Different from the traditional SAE, the PL-SSAE requires all samples in pre-training and the unlabeled samples with pseudo labels in fine-tuning to fully exploit the feature and category information of the unlabeled samples. Empirical evaluations on various benchmark datasets show that the semi-supervised performance of the PL-SSAE is more competitive than that of the SAE, sparse stacked autoencoder (SSAE), semi-supervised stacked autoencoder (Semi-SAE) and semi-supervised stacked autoencoder (Semi-SSAE).<\/jats:p>","DOI":"10.3390\/e25091274","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T09:44:46Z","timestamp":1693388686000},"page":"1274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1028-8162","authenticated-orcid":false,"given":"Jie","family":"Lai","sequence":"first","affiliation":[{"name":"College of Air and Missile Defense, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Air and Missile Defense, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6810-8446","authenticated-orcid":false,"given":"Qian","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Air and Missile Defense, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Quan","sequence":"additional","affiliation":[{"name":"College of Air Traffic Control and Navigation, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0962-0671","authenticated-orcid":false,"given":"Yafei","family":"Song","sequence":"additional","affiliation":[{"name":"College of Air and Missile Defense, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_2","first-page":"153","article-title":"Greedy layer-wise training of deep networks","volume":"19","author":"Bengio","year":"2007","journal-title":"Adv. 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