Unsupervised feature selection through preservation of locality relation

Key Points

• Feature selection enhances data representation by preserving locality, which is crucial for unsupervised learning.
• The proposed method demonstrates notable improvements in data representation for high-dimensional datasets, highlighting locality relation preservation.
• This approach employs unsupervised learning techniques to optimize feature selection and dimensionality reduction for better data analysis.
• Results suggest that preserving locality in feature selection may enhance model performance, while emphasizing further research is needed.