Subspace Learning and Feature Selection via Orthogonal Mapping

Mandanas, Fotios D.; Kotropoulos, Constantine

doi:10.1109/tsp.2020.2967714

Cited by 11 publications

(2 citation statements)

References 33 publications

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“…Let X ∈ R n×d be the high-dimensional data, through subspace learning, we can learn a low-dimensional subspace of X that can well represent the original data [7], [9]. Its formula can be expressed as follows:…”

Section: Proposed Methodsmentioning

confidence: 99%

A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection

Zhang

Song

Sheng

et al. 2022

IEICE Trans. Inf. & Syst.

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Unsupervised Feature selection is an important dimensionality reduction technique to cope with high-dimensional data. It does not require prior label information, and has recently attracted much attention. However, it cannot fully utilize the discriminative information of samples, which may affect the feature selection performance. To tackle this problem, in this letter, we propose a novel discriminative virtual label regression method (DVLR) for unsupervised feature selection. In DVLR, we develop a virtual label regression function to guide the subspace learning based feature selection, which can select more discriminative features. Moreover, a linear discriminant analysis (LDA) term is used to make the model be more discriminative. To further make the model be more robust and select more representative features, we impose the 2,1 -norm on the regression and feature selection terms. Finally, extensive experiments are carried out on several public datasets, and the results demonstrate that our proposed DVLR achieves better performance than several state-of-the-art unsupervised feature selection methods.

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Section: Proposed Methodsmentioning

confidence: 99%

A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection

Zhang

Song

Sheng

et al. 2022

IEICE Trans. Inf. & Syst.

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show abstract

“…When data dimension is too large, it becomes difficult to choose the best solution from possibilities (Kanwal et al. 2021 ; Mandanas and Kotropoulos 2020 ). The goal is to reduce the number of features and improve classification quality, so it is optimized as a single- or multi-objective (Lima et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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Introduction

Lai

Cui

2022

Wireless Networks

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Subspace Learning and Feature Selection via Orthogonal Mapping

Cited by 11 publications

References 33 publications

A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection

A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection

A survey on binary metaheuristic algorithms and their engineering applications

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