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Department","award":["20220201157GX"],"award-info":[{"award-number":["20220201157GX"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Information"],"abstract":"<jats:p>As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature selection methods (SSFS) have been proposed to select a subset of features, but they still have some drawbacks limiting their performance, for e.g., many SSFS methods underutilize the structural distribution information available within labeled and unlabeled data. To address this issue, we proposed a semi-supervised feature selection method based on an adaptive graph with global and local constraints (SFS-AGGL) in this paper. Specifically, we first designed an adaptive graph learning mechanism that can consider both the global and local information of samples to effectively learn and retain the geometric structural information of the original dataset. Secondly, we constructed a label propagation technique integrated with the adaptive graph learning in SFS-AGGL to fully utilize the structural distribution information of both labeled and unlabeled data. The proposed SFS-AGGL method is validated through classification and clustering tasks across various datasets. The experimental results demonstrate its superiority over existing benchmark methods, particularly in terms of clustering performance.<\/jats:p>","DOI":"10.3390\/info15010057","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T10:39:47Z","timestamp":1705487987000},"page":"57","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9828-0319","authenticated-orcid":false,"given":"Yugen","family":"Yi","sequence":"first","affiliation":[{"name":"School of Software, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1224-6414","authenticated-orcid":false,"given":"Gengsheng","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4028-6149","authenticated-orcid":false,"given":"Caixia","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Northeast Normal University, Changchun 130117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,17]]},"reference":[{"key":"ref_1","first-page":"105426","article-title":"Feature-splitting Algorithms for Ultrahigh Dimensional Quantile Regression","volume":"2023","author":"Wen","year":"2023","journal-title":"J. Econom."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112063","DOI":"10.1016\/j.measurement.2022.112063","article-title":"Image feature extraction based on fuzzy restricted Boltzmann machine","volume":"204","author":"Lue","year":"2022","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.patcog.2016.11.003","article-title":"A survey on semi-supervised feature selection methods","volume":"64","author":"Sheikhpour","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s12559-019-09668-6","article-title":"Efficient hybrid nature-inspired binary optimizers for feature selection","volume":"12","author":"Mafarja","year":"2020","journal-title":"Cogn. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111310","DOI":"10.1016\/j.measurement.2022.111310","article-title":"Image feature selection based on orthogonal \u21132,0 norms","volume":"199","author":"Huang","year":"2022","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature selection in machine learning: A new perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113745","DOI":"10.1016\/j.eswa.2020.113745","article-title":"A systematic evaluation of filter Unsupervised Feature Selection methods","volume":"162","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2021.02.034","article-title":"Supervised feature selection using integration of densest subgraph finding with floating forward\u2013backward search","volume":"566","author":"Bhadra","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_9","first-page":"955","article-title":"Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data","volume":"11","author":"Mann","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_10","first-page":"793","article-title":"Joint embedding learning and sparse regression: A framework for unsupervised feature selection","volume":"44","author":"Hou","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"115365","DOI":"10.1016\/j.eswa.2021.115365","article-title":"A feature selection method via analysis of relevance, redundancy, and interaction","volume":"183","author":"Wang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.1016\/j.neucom.2022.04.083","article-title":"A comprehensive survey on recent metaheuristics for feature selection","volume":"494","author":"Dokeroglu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_13","first-page":"1210","article-title":"Structured graph optimization for unsupervised feature selection","volume":"33","author":"Nie","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, Z., and Liu, H. (2007, January 26\u201328). Semi-supervised feature selection via spectral analysis. Proceedings of the 2007 SIAM International Conference on Data Mining; Society for Industrial and Applied Mathematics, Minneapolis, MN, USA.","DOI":"10.1137\/1.9781611972771.75"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107703","DOI":"10.1016\/j.measurement.2020.107703","article-title":"Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models","volume":"158","author":"Ergen","year":"2020","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, X., Song, L., Hou, Y., and Shao, G. (2016, January 10\u201315). Efficient semi-supervised feature selection for VHR remote sensing images. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729383"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"116938","DOI":"10.1016\/j.image.2023.116938","article-title":"Adaptive graph regularization method based on least square regression for clustering","volume":"114","author":"Peng","year":"2023","journal-title":"Signal Process. Image Commun."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chang, X., Nie, F., Yang, Y., and Huang, H. (2014, January 27\u201331). A convex formulation for semi-supervised multi-label feature selection. Proceedings of the AAAI Conference on Artificial Intelligence, Qu\u00e9bec City, QC, Canada.","DOI":"10.1609\/aaai.v28i1.8922"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, G., Nie, F., and Huang, J.Z. (2017, January 19\u201325). Semi-supervised feature selection via rescaled linear regression. Proceedings of the Twenty Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/211"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5756","DOI":"10.1109\/TCYB.2021.3052847","article-title":"Semi supervised feature selection via structured manifold learning","volume":"52","author":"Chen","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107456","DOI":"10.1016\/j.sigpro.2020.107456","article-title":"Structured optimal graph based sparse feature extraction for semi-supervised learning","volume":"170","author":"Liu","year":"2020","journal-title":"Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"131939","DOI":"10.1109\/ACCESS.2020.3009125","article-title":"cACP-2LFS: Classification of anticancer peptides using sequential discriminative model of KSAAP and two-level feature selection approach","volume":"8","author":"Akbar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e13205","DOI":"10.7717\/peerj.13205","article-title":"Inflammatory bowel disease biomarkers of human gut microbiota selected via ensemble feature selection methods","volume":"10","author":"Hacilar","year":"2022","journal-title":"PeerJ"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Rafiq, J.I., and Islam, M.R. (2020). Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors, 20.","DOI":"10.3390\/s20010317"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ins.2020.12.082","article-title":"BELIEF: A distance-based redundancy-proof feature selection method for Big Data","volume":"558","author":"Xiong","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6362","DOI":"10.1109\/TNNLS.2018.2830186","article-title":"Local adaptive projection framework for feature selection of labeled and unlabeled data","volume":"29","author":"Chen","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1109\/TIP.2009.2038764","article-title":"Learning with l1-graph for image analysis","volume":"19","author":"Cheng","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","article-title":"Robust recovery of subspace structures by low-rank representation","volume":"35","author":"Liu","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Singh, R.P., Ojha, D., and Jadon, K.S. (2022). A Survey on Various Representation Learning of Hypergraph for Unsupervised Feature Selection. Data, Engineering and Applications: Select Proceedings of IDEA 2021, Springer.","DOI":"10.1007\/978-981-19-4687-5_6"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1109\/TPAMI.2013.57","article-title":"Sparse subspace clustering: Algorithm, theory, and applications","volume":"35","author":"Elhamifar","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105512","DOI":"10.1016\/j.knosys.2020.105512","article-title":"Subspace clustering by simultaneously feature selection and similarity learning","volume":"193","author":"Zhong","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3338","DOI":"10.1109\/TKDE.2020.2969860","article-title":"Adaptive similarity embedding for unsupervised multi-view feature selection","volume":"33","author":"Wan","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1007\/s13042-020-01079-6","article-title":"Double feature selection algorithm based on low-rank sparse non-negative matrix factorization","volume":"11","author":"Shang","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11063-020-10383-9","article-title":"Joint spectral clustering based on optimal graph and feature selection","volume":"53","author":"Zhu","year":"2021","journal-title":"Neural Process. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110897","DOI":"10.1016\/j.measurement.2022.110897","article-title":"An acoustic signal cavitation detection framework based on XGBoost with adaptive selection feature engineering","volume":"192","author":"Sha","year":"2022","journal-title":"Measurement"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.ins.2022.11.156","article-title":"Unsupervised feature selection through combining graph learning and \u21132, 0-norm constraint","volume":"622","author":"Zhu","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.neunet.2023.04.044","article-title":"Joint feature selection and optimal bipartite graph learning for subspace clustering","volume":"164","author":"Mei","year":"2023","journal-title":"Neural Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107375","DOI":"10.1016\/j.patcog.2020.107375","article-title":"Unsupervised feature selection with adaptive multiple graph learning","volume":"105","author":"Zhou","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.neucom.2020.01.044","article-title":"Multi-view feature selection via nonnegative structured graph learning","volume":"387","author":"Bai","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4232","DOI":"10.1109\/TCYB.2022.3160244","article-title":"Balanced spectral feature selection","volume":"53","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108299","DOI":"10.1016\/j.patcog.2021.108299","article-title":"Graph regularized locally linear embedding for unsupervised feature selection","volume":"122","author":"Miao","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xie, G.B., Chen, R.B., Lin, Z.Y., Gu, G.S., Yu, J.R., Liu, Z., Cui, J., Lin, L., and Chen, L. (2023). Predicting lncRNA\u2013disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation. Brief. Bioinform., 24.","DOI":"10.1093\/bib\/bbac595"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ins.2020.03.094","article-title":"A robust graph-based semi-supervised sparse feature selection method","volume":"531","author":"Sheikhpour","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"192108","DOI":"10.1007\/s11432-020-3063-0","article-title":"Semi-supervised local feature selection for data classification","volume":"64","author":"Li","year":"2021","journal-title":"Sci. China Inf. Sci."},{"key":"ref_45","unstructured":"Jiang, B., Wu, X., Zhou, X., Liu, Y., Cohn, A.G., Sheng, W., and Chen, H. (2022). Semi-supervised multiview feature selection with adaptive graph learning. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.neucom.2022.02.038","article-title":"Sparse and low-dimensional representation with maximum entropy adaptive graph for feature selection","volume":"485","author":"Shang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.ins.2022.07.102","article-title":"Adaptive graph learning for semi-supervised feature selection with redundancy minimization","volume":"609","author":"Lai","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"109243","DOI":"10.1016\/j.knosys.2022.109243","article-title":"Semi-supervised feature selection via adaptive structure learning and constrained graph learning","volume":"251","author":"Lai","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/TKDE.2018.2810286","article-title":"Semi-supervised feature selection via insensitive sparse regression with application to video semantic recognition","volume":"30","author":"Luo","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neunet.2018.12.008","article-title":"Learning a discriminant graph-based embedding with feature selection for image categorization","volume":"111","author":"Zhu","year":"2019","journal-title":"Neural Netw."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112795","DOI":"10.1016\/j.cam.2020.112795","article-title":"Construction of the similarity matrix for the spectral clustering method: Numerical experiments","volume":"375","author":"Favati","year":"2020","journal-title":"J. Comput. Appl. Math."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"110411","DOI":"10.1016\/j.knosys.2023.110411","article-title":"Adaptive Manifold Graph representation for Two-Dimensional Discriminant Projection","volume":"266","author":"Qu","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"28054","DOI":"10.1007\/s10489-023-04868-y","article-title":"Adaptive graph regularized non-negative matrix factorization with self-weighted learning for data clustering","volume":"53","author":"Ma","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yang, S., Wen, J., Zhan, X., and Kifer, D. (2019, January 4\u20138). ET-lasso: A new efficient tuning of lasso-type regularization for high-dimensional data. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330910"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Huang, S., Xu, Z., and Wang, F. (2017, January 14\u201319). Nonnegative matrix factorization with adaptive neighbors. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7965893"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"8792","DOI":"10.1109\/ACCESS.2017.2699741","article-title":"Structure preserving non-negative feature self-representation for unsupervised feature selection","volume":"5","author":"Zhou","year":"2017","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107749","DOI":"10.1016\/j.knosys.2021.107749","article-title":"Feature selection based on non-negative spectral feature learning and adaptive rank constraint","volume":"236","author":"Shang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_58","unstructured":"Martinez, A., and Benavente, R. (1998). The AR Face Database: CVC Technical Report, Computer Vision Center."},{"key":"ref_59","unstructured":"Sim, T., Baker, S., and Bsat, M. (2002, January 20\u201321). The CMU pose, illumination, and expression (PIE) database. Proceedings of the Fifth IEEE International Conference on Automatic Face Gesture Recognition, Washington, DC, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1016\/j.patcog.2010.01.020","article-title":"Online finger-knuckle-print verification for personal authentication","volume":"43","author":"Zhang","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_61","unstructured":"Samaria, F.S., and Harter, A.C. (1994, January 21\u201323). Parameterisation of a stochastic model for human face identification. Proceedings of the 1994 IEEE Workshop on Applications of Computer Vision, Seattle, WA, USA."},{"key":"ref_62","unstructured":"Nene, S.A., Nayar, S.K., and Murase, H. (1996). Columbia Object Image Library (COIL-20), Columbia University."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"119029","DOI":"10.1016\/j.ins.2023.119029","article-title":"RRNMF-MAGL: Robust regularization non-negative matrix factorization with multi-constraint adaptive graph learning for dimensionality reduction","volume":"640","author":"Yi","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_64","unstructured":"Blake, C.L., and Merz, C.J. (1998). UCI Repository of Machine Learning Databases, Department of Information and Computer Science, University of California."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1109\/TIP.2022.3147046","article-title":"High-order correlation preserved incomplete multi-view subspace clustering","volume":"31","author":"Li","year":"2022","journal-title":"IEEE Trans. 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