Semi-supervised feature selection analysis with structured multi-view sparse regularization

Shi, Caijuan; Duan, Changyu; Gu, Zhibin; An, Gaoyun; Zhao, Ruizhen

doi:10.1016/j.neucom.2018.10.027

Cited by 29 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For further considering the importance of different views, several multiview schemes have been proposed to learn weights for different views [12,14,20,21,33]. In [33], Xu et al proposed a weighted multiview clustering with feature selection method (WMCFS) to improve the clustering accuracy via designing two weighting schemes.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with feature extraction, feature selection methods can preserve the physical meaning of original features. According to the label availability, the feature selection methods are classified into supervised ones [3,16], semi-supervised ones [22,35], and unsupervised ones [11,30]. Among the three kinds of methods, the supervised methods are most likely to select the better discriminative feature subset because they can utilize the label information and consider the correlation between labels and features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust multiview feature selection via view weighted

Zhong

et al. 2020

Multimed Tools Appl

View full text Add to dashboard Cite

In recent years, combining the multiple views of data to perform feature selection has been popular. As the different views are the descriptions from different angles of the same data, the abundant information coming from multiple views instead of the single view can be used to improve the performance of identification. In this paper, through the view weighted strategy, we propose a novel robust supervised multiview feature selection method, in which the robust feature selection is performed under the effect of 2 1 -norm. The proposed model has the following advantages. Firstly, different from the commonly used view concatenation that is liable to ignore the physical meaning of features and cause over-fitting, the proposed method divides the original space into several subspaces and performs feature selection in the subspaces, which can reduce the computational complexity. Secondly, the proposed method assigns different weights to views adaptively according to their importance, which shows the complementarity and the specificity of views. Then, the iterative algorithm is given to solve the proposed model, and in each iteration, the original large-scale problem is split into the small-scale subproblems due to the divided original space. The performance of the proposed method is compared with several related state-of-the-art methods on the widely used multiview datasets, and the experimental results demonstrate the effectiveness of the proposed method.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust multiview feature selection via view weighted

Zhong

et al. 2020

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…Based on the existence or absence of labels, the existing feature selection methods can be roughly divided into three categories: supervised feature selection (Taher et al 2019), unsupervised feature selection (Shi et al 2018) and semi-supervised feature selection (Wang and Wang 2017). Supervised feature selection can take advantage of feature information and label information to achieve high accuracy when the data sets all have labels.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised multi-label feature selection with local logic information preserved

Zhang

Yang

et al. 2021

Adv. in Comp. Int.

View full text Add to dashboard Cite

In reality, like single-label data, multi-label data sets have the problem that only some have labels. This is an excellent challenge for multi-label feature selection. This paper combines the logistic regression model with graph regularization and sparse regularization to form a joint framework (SMLFS) for semi-supervised multi-label feature selection. First of all, the regularization of the feature graph is used to explore the geometry structure of the feature, to obtain a better regression coefficient matrix, which reflects the importance of the feature. Second, the label graph regularization is used to extract the available label information, and constrain the regression coefficient matrix, so that the regression coefficient matrix can better fit the label information. Third, the L 2, p -norm 0 < p ≤ 1 constraint is used to ensure the sparsity of the regression coefficient matrix so that it is more convenient to distinguish the importance of features. In addition, an iterative updating algorithm with convergence is designed and proved to solve the above problems. Finally, the proposed method is validated on eight classic multi-label data sets, and the experimental results show the effectiveness of the proposed algorithm.

show abstract

“…Certainly, the research work on feature selection based on semi-supervised is also studied. Shi et al [10] developed the structured multi-view hessian sparse semi-supervise feature selection framework, and the feature selection performance is improved by the proposed algorithm. Yu et al [11] proposed an adaptive semi-supervised feature selection, and the graph-based constraint is used to predict accurate labels for unlabeled data, and the experimental results show the method can obtain the smaller feature subsets and higher classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Feature Selection and Clustering Optimization Based on Improved Differential Evolution

Dong

2019

IEEE Access

View full text Add to dashboard Cite

The feature selection method based on supervised learning has been widely studied and applied to the field of machine learning and data mining. But unsupervised feature selection is still a tricky area of research because the unavailability of the label information, especially for clustering tasks. Irrelevant features and redundant features in the original data seriously block the discovery of clustering structure and weaken the performance of the subsequent classification. In order to address this problem, the unsupervised feature selection and clustering algorithm based on the evolutionary computing framework is proposed in this paper. First, the binary differential evolution algorithm is constructed for unsupervised feature selection. Specifically, the individuals of the population are used to characterize the feature subspaces and the improved Laplacian model is designed to measure the local manifold structure of each individual. Subsequently, the approximate optimal manifold structure and the corresponding feature subset are obtained. Then, the continuous differential evolutionary algorithm is executed on the optimized feature subset, in which the individual representation strategy and the integrated individual measure function are designed for clustering. Moreover, the predicted pseudo-labels are utilized to classify and further verify the validity of clustering. The experimental results demonstrate that the proposed framework outperforms the most state-of-the-art methods.

show abstract

Semi-supervised feature selection analysis with structured multi-view sparse regularization

Cited by 29 publications

References 32 publications

Robust multiview feature selection via view weighted

Robust multiview feature selection via view weighted

Semi-supervised multi-label feature selection with local logic information preserved

Unsupervised Feature Selection and Clustering Optimization Based on Improved Differential Evolution

Contact Info

Product

Resources

About