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Cluster Density Properties Define a Graph for Effective Pattern Feature Selection
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DOI：10.1109/ACCESS.2020.2981265.png)
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Abstract
En 中文
Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. It addresses the curse of dimensionality by determining a small set of features to represent high-dimensional data without significant or noticeable loss of information. The purpose of this study is to develop and investigate a new unsupervised feature selection method which uses the k-influence space concept and subspace learning to map features onto a weighted graph and rank them by importance according to the PageRank graph centrality measure. The graph design in this method promotes feature relevance, downgrades redundancy, and is robust to outliers and cluster imbalances. In K-Means classification experiments using the ASU feature selection testing datasets, the method produces better accuracy and normalized mutual information results than state-of-the-art unsupervised feature selection algorithms. In a further evaluation, using a dataset of over 14,000 tweets, conventional classification of features selected by the method gave better sentiment analysis results than deep learning feature selection and classification.
Keywords:
Feature extraction
Clustering algorithms
Redundancy
Manifolds
Correlation
Data analysis
Weight measurement
Feature selection
projected-clustering
influence-space
graph centrality
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