Group-preserving label-specific feature selection for multi-label learning

Wu, Hanrui; Jiang, Min; Liu, Jinghua; Li, Shaozi; Tang, Yong; Long, Jinyi

doi:10.1016/j.eswa.2022.118861

Cited by 32 publications

(7 citation statements)

References 56 publications

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“…Experimental findings reveal that our proposed supervised MEFS method experiences an increase in time complexity as the number of source domain samples increases, while it cannot guarantee optimal feature selection on the target domain, thus exhibiting limitations in both performance and efficiency. Recently, researchers have proposed efficient and unsupervised multi-label feature selection methods (Zhang et al, 2020b , 2023 ) that demonstrate promising performance when dealing with larger sample sets. These feature selection methods have the potential to be expanded and adapted for the selection of more complex MI-EEG feature representations.…”

Section: Discussionmentioning

confidence: 99%

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification

Luo

2023

Front. Neurosci.

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IntroductionMotor imagery electroencephalograph (MI-EEG) has attracted great attention in constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and convenience. However, only a few MI-EEG classification methods have been recently been applied to BCIs, mainly because they suffered from sample variability across subjects. To address this issue, the cross-subject scenario based on domain adaptation has been widely investigated. However, existing methods often encounter problems such as redundant features and incorrect pseudo-label predictions in the target domain.MethodsTo achieve high performance cross-subject MI-EEG classification, this paper proposes a novel method called Dual Selections based Knowledge Transfer Learning (DS-KTL). DS-KTL selects both discriminative features from the source domain and corrects pseudo-labels from the target domain. The DS-KTL method applies centroid alignment to the samples initially, and then adopts Riemannian tangent space features for feature adaptation. During feature adaptation, dual selections are performed with regularizations, which enhance the classification performance during iterations.Results and discussionEmpirical studies conducted on two benchmark MI-EEG datasets demonstrate the feasibility and effectiveness of the proposed method under multi-source to single-target and single-source to single-target cross-subject strategies. The DS-KTL method achieves significant classification performance improvement with similar efficiency compared to state-of-the-art methods. Ablation studies are also conducted to evaluate the characteristics and parameters of the proposed DS-KTL method.

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Section: Discussionmentioning

confidence: 99%

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification

Luo

2023

Front. Neurosci.

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“…CLML [9] algorithm proposed by Li et al first uses a norm in the LSF framework to extract the common features of the labels. Subsequently, the GLFS [21] algorithm proposed by Zhang et al builds a grouppreserving optimization framework for feature selection by learning the common features of similar labels and the private features of each label using K-means clustering. Based on the above analysis, we adopt a causal learning algorithm to learn asymmetric LC among labels in LSF learning framework.…”

Section: Related Workmentioning

confidence: 99%

Causality-Driven Common and Label-Specific Features Learning

Xu,

Zhang,

Wang

et al. 2024

JAI

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In multi-label learning, the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data. The classification performance and robustness of the model are effectively improved. Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation. It is well known that the correlation between labels is asymmetric. However, existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels. Based on this, this paper proposes a Causality-driven Common and Labelspecific Features Learning, named CCSF algorithm. Firstly, the causal learning algorithm GSBN is used to calculate the asymmetric correlation between labels. Then, in the optimization, both l 2,1 -norm and l 1 -norm are used to select the corresponding features, respectively. Finally, it is compared with six state-of-the-art algorithms on nine datasets. The experimental results prove the effectiveness of the algorithm in this paper.

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“…Since the multilabel learning framework is trained using known features and labeled samples, based on this feature researchers have proposed the following five commonly used evaluation metrics in multilabel learning evaluation: the Average Precision (AP), Coverage (CV), Hamming Loss (HL), Ranking Loss (RL), and One-Error (OE), [12] to judge the effectiveness of the algorithm.…”

Section: Multi-label Learning Evaluation Metricmentioning

confidence: 99%

Face Image Recognition Algorithm Based on Label Complementation

Jiakang Tang,

Lin Cui

2023

Journal of Internet Technology

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<p>In face image recognition, labels play a fairly important role in recognition and classification, and rich and perfect labels can greatly improve the accuracy rate. However, it is almost impossible for the labels in the image to be recognized to describe the image completely and accurately. At the same time, the data obtained when feature extraction is performed on an image inevitably extracts a large amount of redundant and useless information at the same time, which affects the generalization performance of the model. Accordingly, we propose a face image recognition algorithm based on label completion in multi label learning. First, the SVD algorithm is used to remove redundant and useless information from the features of the original data by dimensionality reduction operation to obtain simplified sample attribute information, and the label completion algorithm is used to supplement the labels of the images using the extracted feature information. Finally the obtained label data as complete as possible is put into the extreme learning machine to construct the face recognition model and give the prediction results of the images. Experiments on the ORL dataset demonstrate that the algorithm can achieve good recognition results.</p> <p> </p>

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Group-preserving label-specific feature selection for multi-label learning

Cited by 32 publications

References 56 publications

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification

Causality-Driven Common and Label-Specific Features Learning

Face Image Recognition Algorithm Based on Label Complementation

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