Bi-level spectral feature selection

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorHu, Zebiao
dc.contributor.authorWang, Jian
dc.contributor.authorZhang, Kai
dc.contributor.authorPedrycz, Witold
dc.contributor.authorPal, Nikhil R.
dc.date.accessioned2025-04-18T08:45:19Z
dc.date.available2025-04-18T08:45:19Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractUnsupervised feature selection (UFS) aims to learn an indicator matrix relying on some characteristics of the high-dimensional data to identify the features to be selected. However, traditional unsupervised methods perform only at the feature level, i.e., they directly select useful features by feature ranking. Such methods do not pay any attention to the interaction information with other tasks such as classification, which severely degrades their feature selection performance. In this article, we propose an UFS method which also takes into account the classification level, and selects features that perform well both in clustering and classification. To achieve this, we design a bi-level spectral feature selection (BLSFS) method, which combines classification level and feature level. More concretely, at the classification level, we first apply the spectral clustering to generate pseudolabels, and then train a linear classifier to obtain the optimal regression matrix. At the feature level, we select useful features via maintaining the intrinsic structure of data in the embedding space with the learned regression matrix from the classification level, which in turn guides classifier training. We utilize a balancing parameter to seamlessly bridge the classification and feature levels together to construct a unified framework. A series of experiments on 12 benchmark datasets are carried out to demonstrate the superiority of BLSFS in both clustering and classification performance.
dc.identifier.citationHu, Z., Wang, J., Zhang, K., Pedrycz, W., & Pal, N. R. (2024). Bi-Level Spectral Feature Selection. IEEE Transactions on Neural Networks and Learning Systems.
dc.identifier.doi10.1109/TNNLS.2024.3408208
dc.identifier.endpage15
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.scopus2-s2.0-85196733844
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1109/TNNLS.2024.3408208
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6603
dc.identifier.wosWOS:001252456000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE-INST electrical electronics engineers
dc.relation.ispartofIEEE transactions on neural networks and learning systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFeature Extraction
dc.subjectTask Analysis
dc.subjectPetroleum
dc.subjectClustering Algorithms Classification Algorithms
dc.subjectOptimization
dc.subjectLinear Programming
dc.subjectBi-level spectral feature selection (BLSFS)
dc.subjectClassification Level
dc.subjectFeature Level
dc.subjectHigh-Dimensional Data
dc.subjectUnsupervised Learning
dc.titleBi-level spectral feature selection
dc.typeArticle

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