Unsupervised feature selection based on local structure learning

2019 ◽  
Vol 79 (45-46) ◽  
pp. 34571-34585
Author(s):  
Yanbei Liu ◽  
Lei Geng ◽  
Fang Zhang ◽  
Jun Wu ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Structure Learning ◽  
Unsupervised Feature Selection ◽  
Local Structure Learning

scholarly journals Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning

2019 ◽  
Vol 3 (2) ◽  
pp. 115 ◽  
Author(s):  
Jiaye Li ◽  
Guoqiu Wen ◽  
Jiangzhang Gan ◽  
Leyuan Zhang ◽  
Shanwen Zhang
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Structure Learning ◽  
Kernel Method ◽  
Optimization Method ◽  
Local Similarity ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Kernel Space ◽  
Local Structure Learning

In this paper, we propose a new unsupervised feature selection algorithm by considering the nonlinear and similarity relationships within the data. To achieve this, we apply the kernel method and local structure learning to consider the nonlinear relationship between features and the local similarity between features. Specifically, we use a kernel function to map each feature of the data into the kernel space. In the high-dimensional kernel space, different features correspond to different weights, and zero weights are unimportant features (e.g. redundant features). Furthermore, we consider the similarity between features through local structure learning, and propose an effective optimization method to solve it. The experimental results show that the proposed algorithm achieves better performance than the comparison algorithm.

scholarly journals An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3627
Author(s):  
Bo Jin ◽  
Chunling Fu ◽  
Yong Jin ◽  
Wei Yang ◽  
Shengbin Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Gene Selection ◽  
Dimensional Space ◽  
Original Data ◽  
Global Structure ◽  
Biological Data ◽  
Special Treatment ◽  
Selection Scheme ◽  
Unsupervised Feature Selection

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

scholarly journals Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection

2019 ◽  
Vol 33 ◽  
pp. 5101-5108 ◽  
Author(s):  
Chang Tang ◽  
Xinzhong Zhu ◽  
Xinwang Liu ◽  
Lizhe Wang
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Feature Subset ◽  
Unsupervised Feature Selection ◽  
Semantic Learning ◽  
Common Information ◽  
Semantic Label ◽  
Similarity Graph ◽  
Feature Projection ◽  
Norm Constraint

Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2,1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.

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