Feature Selection and Kernel Learning for Local Learning-Based Clustering

Zeng, Hong; Cheung, Yiu-ming

doi:10.1109/tpami.2010.215

Cited by 210 publications

(17 citation statements)

References 30 publications

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“…(3) Multi-parameters from multi-network: All parameters extracted from all network layers are used as different input features to the classifier. Then for each set, various feature select methods including Correlation-based Feature Selection (CFS) (Guyon et al, 2002 ), Dependence Guided Unsupervised Feature Selection (DGUFS) (Zhu et al, 2017 ), Fisher (Gu et al, 2012 ), Feature Selective Validation (FSV) (Bradley and Mangasarian, 1999 ), Locality-Constrained Linear Coding Feature Select (LLCFS) (Zeng and Cheung, 2011 ), and minimum-redundancy maximum-relevance (mRMR) (Peng et al, 2005 ) are used to sort the features to obtain the feature sequence for each set. According to the obtained feature sequence, select the different number of features in order (i.e., the first one feature, the first two features, the first three features.)…”

Section: Resultsmentioning

confidence: 99%

Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach

Zhu

Cai

et al. 2020

Front. Neurosci.

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A novel analytical framework combined fuzzy learning and complex network approaches is proposed for the identification of Alzheimer's disease (AD) with multichannel scalp-recorded electroencephalograph (EEG) signals. Weighted visibility graph (WVG) algorithm is first applied to transform each channel EEG into network and its topological parameters were further extracted. Statistical analysis indicates that AD and normal subjects show significant difference in the structure of WVG network and thus can be used to identify Alzheimer's disease. Taking network parameters as input features, a Takagi-Sugeno-Kang (TSK) fuzzy model is established to identify AD's EEG signal. Three feature sets-single parameter from multi-networks, multi-parameters from single network, and multi-parameters from multi-networks-are considered as input vectors. The number and order of input features in each set is optimized with various feature selection methods. Classification results demonstrate the ability of network-based TSK fuzzy classifiers and the feasibility of three input feature sets. The highest accuracy that can be achieved is 95.28% for single parameter from four networks, 93.41% for three parameters from single network. In particular, multi-parameters from the multi-networks set obtained the best result. The highest accuracy, 97.12%, is achieved with five features selected from four networks. The combination of network and fuzzy learning can highly improve the efficiency of AD's EEG identification.

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Section: Resultsmentioning

confidence: 99%

Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach

Zhu

Cai

et al. 2020

Front. Neurosci.

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“…Eight popular state-of-the-art feature selection methods were analyzed as representatives of different FS approaches, including: Chi-square test (Chi2), Infinite latent feature selection (ILFS) 32 , Relief-F (ReliefF) 33 , Laplacian score (LS) 34 , Local learning-based clustering with feature selection ILFS (LLCFS) 35 , Minimum redundancy-maximum relevance (mRMR) 36 , Random forests (RF) 37,38 , and Gradient boosting machine (GBM) 39 . Supplementary Table S3 describes their categories and computing complexity.…”

Section: Methodsmentioning

confidence: 99%

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

Liu

et al. 2018

Sci Rep

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Acute Kidney Injury (AKI) is a common complication encountered among hospitalized patients, imposing significantly increased cost, morbidity, and mortality. Early prediction of AKI has profound clinical implications because currently no treatment exists for AKI once it develops. Feature selection (FS) is an essential process for building accurate and interpretable prediction models, but to our best knowledge no study has investigated the robustness and applicability of such selection process for AKI. In this study, we compared eight widely-applied FS methods for AKI prediction using nine-years of electronic medical records (EMR) and examined heterogeneity in feature rankings produced by the methods. FS methods were compared in terms of stability with respect to data sampling variation, similarity between selection results, and AKI prediction performance. Prediction accuracy did not intrinsically guarantee the feature ranking stability. Across different FS methods, the prediction performance did not change significantly, while the importance rankings of features were quite different. A positive correlation was observed between the complexity of suitable FS method and sample size. This study provides several practical implications, including recognizing the importance of feature stability as it is desirable for model reproducibility, identifying important AKI risk factors for further investigation, and facilitating early prediction of AKI.

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“…LLCFS [ 35 ] integrates local structure learning and feature selection into a unified framework. Specifically, LLCFS embeds weighted features into the regularization term of the local clustering learning algorithm and selects features according to their weight;…”

Section: Methodsmentioning

confidence: 99%

“…URAFS [9] embeds the local geometric structure of data into the manifold learning framework by introducing the graph regularization term based on the principle of maximum entropy into the GURM model, leading to the irrelevant features of the original data being filtered out; • UDFS [25] embeds discriminative analysis and the 2,1 -norm into the feature selection framework to select discriminative features and informative features; • SPEC [19] is a unified feature selection framework based on graph theory and is used to select relevant features by combining supervised feature selection and unsupervised feature selection; • NDFS [28] utilizes the discriminant information and correlation of features to select feature subsets. Specifically, the method combines cluster labels learned by the spectrum clustering algorithm with the feature selection matrix to finally select the most discriminant features; • LLCFS [35] integrates local structure learning and feature selection into a unified framework. Specifically, LLCFS embeds weighted features into the regularization term of the local clustering learning algorithm and selects features according to their weight; • JELSR [27] is based on an unsupervised learning structure and combines embedding learning with sparse regression to select features.…”

Section: Contrast Algorithmmentioning

confidence: 99%

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

Jin

et al. 2021

Sensors

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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.

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Feature Selection and Kernel Learning for Local Learning-Based Clustering

Cited by 210 publications

References 30 publications

Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach

Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

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

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