Unsupervised Feature Selection With Ordinal Preserving Self-Representation

Dai, Jiangyan; Chen, Yuqi; Yi, Yugen; Bao, Jining; Wang, Lei; Zhou, Wei; Lei, Gang

doi:10.1109/access.2018.2878855

Cited by 9 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several methods have been proposed by using the selfrepresentation based reconstruction error function [2], [11], [12], [21], [22], [24]- [26], [38], [39], where each feature is reconstructed be other features linear combination according to…”

Section: B Reconstruction Based Methodsmentioning

confidence: 99%

“…On the one hand, the original data should be approximated only from representative samples and relevant features to achieve more faithful and interpretable results. However, the selfrepresentation based methods only take the relevant features from all the candidate samples [11], [21], [22], [24]- [26]. While the matrix factorization and dictionary learning methods estimate the mix-signed and less interpretable coefficients or the basis [14], [15], [18], [20], [27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection

Zhao

Wei

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this paper, we present a novel Local Sensitive Dual Concept Learning (LSDCL) method for the task of unsupervised feature selection. We first reconstruct the original data matrix by the proposed dual concept learning model, which inherits the merit of co-clustering based dual learning mechanism for more interpretable and compact data reconstruction. We then adopt the local sensitive loss function, which emphasizes more on most similar pairs with small errors to better characterize the local structure of data. In this way, our method can select features with better clustering results by more compact data reconstruction and more faithful local structure preserving. An iterative algorithm with convergence guarantee is also developed to find the optimal solution. We fully investigate the performance improvement by the newly developed terms, individually and simultaneously. Extensive experiments on benchmark datasets further show that LSDCL outperforms many state-of-the-art unsupervised feature selection algorithms.

show abstract

Section: B Reconstruction Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection

Zhao

Wei

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The method proposed by Fan et al [34] can select more informative features and learn the structure information of data points at the same time. Dai et al [35] proposed method that each feature is represented by linear combination of other features while maintaining the local geometrical structure and the ordinal locality of original data.…”

Section: A Unsupervised Feature Selection Methodsmentioning

confidence: 99%

Unsupervised Feature Selection via Multi-step Markov Transition Probability

Min,

Ye,

Tian

et al. 2020

Preprint

View full text Add to dashboard Cite

Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good results, in which the relationships between adjacent data points are mainly concerned. But the possible associations between data pairs that are may not adjacent are always neglected. Different from previous methods, we propose a novel and very simple approach for unsupervised feature selection, named MMFS (Multi-step Markov transition probability for Feature Selection). The idea is using multistep Markov transition probability to describe the relation between any data pair. Two ways from the positive and negative viewpoints are employed respectively to keep the data structure after feature selection. From the positive viewpoint, the maximum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. Then, the features which can keep the compact data structure are selected. From the viewpoint of negative, the minimum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. On the contrary, the features that least maintain the loose data structure are selected. And the two ways can also be combined. Thus three algorithms are proposed. Our main contributions are a novel feature section approach which uses multi-step transition probability to characterize the data structure, and three algorithms proposed from the positive and negative aspects for keeping data structure. The performance of our approach is compared with the state-of-the-art methods on eight real-world data sets, and the experimental resultsshow that the proposed MMFS is effective in unsupervised feature selection.

show abstract

“…This method uses Euclidean distance to convert to conditional probability, accordingly explaining high-dimensional data through normal distribution and explaining the similarity between points [44], as shown in (13). = , the result can be obtained, and is shown in (14). Finally, the SNE algorithm also applies Kullback-Leibler divergence (KLD) to express the degree of similarity between the two distributions.…”

Section: ) Stochastic Neighbor Embeddingmentioning

confidence: 99%

“…In order to solve these problems, the selection of features becomes more and more critical. Using typical methods such as ReliefF, symmetrical uncertainty (SU) and fast correlation-based filter (FCBF) for feature selection can undoubtedly reduce the complexity of calculation and database size so that the algorithm will not change the original features [14], [15]. Compared with the other categories, feature extraction projects important features to facilitate visual observation and can reorganize subspaces and retain the original space's data structure.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Fault Classification Based on ROC Curve and t-SNE

Lee

Lin

2021

IEEE Access

View full text Add to dashboard Cite

This paper proposes a novel fault classification method with application to induction motors, which is based on integrating and combining with receiver operating characteristic (ROC) curve and tdistribution stochastic neighbor embedding (t-SNE). According to the feature selection methods of ReliefF, symmetrical uncertainty (SU), and fast correlation-based filter (FCBF), the significant features were verified. Additionally, support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT) are also considered as classifiers to identify the simulation results. To begin with, the current signals obtained from distinctive four topologies of working conditions of the motor, which includes healthy, bearing damage, broken rotor bar, and short circuit in stator windings, respectively. The potential feature set is extracted by using Hilbert-Huang transform (HHT) technique. Then, three feature selection methods are adopted to select three optimal feature subsets from the original feature set. Finally, the classification accuracy (ACC) and ROC curve are used to demonstrate the capability of classifiers' recognition. The results showed that the optimal feature subsets significantly reduce the number of selected features and improve the classification ACC and area under the curve (AUC) compared with the original feature set. In conclusion, the proposed method can downgrade the data, demonstrate the scatter plot more intuitively, and identify various types of faults, unlike with other fault diagnosis literature.

show abstract

Unsupervised Feature Selection With Ordinal Preserving Self-Representation

Cited by 9 publications

References 40 publications

Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection

Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection

Unsupervised Feature Selection via Multi-step Markov Transition Probability

Induction Motor Fault Classification Based on ROC Curve and t-SNE

Contact Info

Product

Resources

About