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 Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020 ◽  
Vol 21 (S18) ◽  
Author(s):  
Sudipta Acharya ◽  
Laizhong Cui ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Marker Gene ◽  
Biological Data ◽  
Protein Interaction Data ◽  
Marker Genes ◽  
Data Sets ◽  
Gene Markers ◽  
Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

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.

scholarly journals Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Na Yu ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Juan Wang ◽  
Junliang Shang
Keyword(s):  
Feature Selection ◽  
Matrix Factorization ◽  
Gene Selection ◽  
Matrix Decomposition ◽  
Original Data ◽  
Data Representation ◽  
Abnormal Expression ◽  
Sample Points ◽  
Laplacian Regularization ◽  
Non Negative Matrix Factorization

Abstract Background As one of the most popular data representation methods, non-negative matrix decomposition (NMF) has been widely concerned in the tasks of clustering and feature selection. However, most of the previously proposed NMF-based methods do not adequately explore the hidden geometrical structure in the data. At the same time, noise and outliers are inevitably present in the data. Results To alleviate these problems, we present a novel NMF framework named robust hypergraph regularized non-negative matrix factorization (RHNMF). In particular, the hypergraph Laplacian regularization is imposed to capture the geometric information of original data. Unlike graph Laplacian regularization which captures the relationship between pairwise sample points, it captures the high-order relationship among more sample points. Moreover, the robustness of the RHNMF is enhanced by using the L2,1-norm constraint when estimating the residual. This is because the L2,1-norm is insensitive to noise and outliers. Conclusions Clustering and common abnormal expression gene (com-abnormal expression gene) selection are conducted to test the validity of the RHNMF model. Extensive experimental results on multi-view datasets reveal that our proposed model outperforms other state-of-the-art methods.

scholarly journals Unsupervised feature selection under perturbations: meeting the challenges of biological data

2007 ◽  
Vol 23 (24) ◽  
pp. 3343-3349 ◽  
Author(s):  
Roy Varshavsky ◽  
Assaf Gottlieb ◽  
David Horn ◽  
Michal Linial
Keyword(s):  
Feature Selection ◽  
Biological Data ◽  
Unsupervised Feature Selection

COMBINING FEATURE SELECTION WITH EXTRACTION: UNSUPERVISED FEATURE SELECTION BASED ON PRINCIPAL COMPONENT ANALYSIS

2009 ◽  
Vol 18 (06) ◽  
pp. 883-904
Author(s):  
YUN LI ◽  
BAO-LIANG LU ◽  
TENG-FEI ZHANG
Keyword(s):  
Feature Selection ◽  
Principal Components ◽  
Nearest Neighbor ◽  
Dimensional Space ◽  
Image Data ◽  
Principal Component ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Unsupervised Feature Selection ◽  
Linear Feature

Principal components analysis (PCA) is a popular linear feature extractor, and widely used in signal processing, face recognition, etc. However, axes of the lower-dimensional space, i.e., principal components, are a set of new variables carrying no clear physical meanings. Thus we propose unsupervised feature selection algorithms based on eigenvectors analysis to identify critical original features for principal component. The presented algorithms are based on k-nearest neighbor rule to find the predominant row components and eight new measures are proposed to compute the correlation between row components in transformation matrix. Experiments are conducted on benchmark data sets and facial image data sets for gender classification to show their superiorities.

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