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A Survey on Sparse Learning Models for Feature Selection
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DOI：10.1109/TCYB.2020.2982445.png)
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Abstract
En 中文
Feature selection is important in both machine learning and pattern recognition. Successfully selecting informative features can significantly increase learning accuracy and improve result comprehensibility. Various methods have been proposed to identify informative features from high-dimensional data by removing redundant and irrelevant features to improve classification accuracy. In this article, we systematically survey existing sparse learning models for feature selection from the perspectives of individual sparse feature selection and group sparse feature selection, and analyze the differences and connections among various sparse learning models. Promising research directions and topics on sparse learning models are analyzed.
Keywords:
Feature extraction
Mathematical model
Data models
Computational modeling
Correlation
Analytical models
Optimization
Feature selection
group sparse
high-dimensional data
sparse learning models
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