[1]ZHU Xingyu,CHEN Xiuhong.Joint uncorrelated regression and non-negative spectral analysis for unsupervised feature selection[J].CAAI Transactions on Intelligent Systems,2022,17(2):303-313.[doi:10.11992/tis.202012033]
Copy
Joint uncorrelated regression and non-negative spectral analysis for unsupervised feature selection
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 2
Page number:
303-313
Column:
学术论文—机器感知与模式识别
Public date:
2022-03-05
- Title:
- Joint uncorrelated regression and non-negative spectral analysis for unsupervised feature selection
- Keywords:
- uncorrelated regression; non-negative spectral analysis; redundant features; local structure learning; unsupervised learning; adaptive graph; feature selection; discriminant feature
- CLC:
- TP391.4
- DOI:
- 10.11992/tis.202012033
- Abstract:
- Unsupervised feature selection is an essential preprocessing step in the data mining and pattern recognition tasks of unlabeled high-dimensional data. However, most existing feature selection methods ignore the correlation between data features and select features with high redundancy and low discrimination. This paper proposes an unsupervised feature selection method based on joint uncorrelated regression and non-negative spectral analysis (Joint uncorrelated regression and nonnegative spectral analysis for unsupervised feature selection). It adaptively and dynamically determines the similarity relationship between data while selecting uncorrelated and discriminant features, so that more accurate data structure and label information can be obtained. Moreover, the generalized uncorrelated constraints in the model can avoid trivial solutions, so this method has the advantages of two feature selection methods: uncorrelated regression and non-negative spectral clustering. An efficient algorithm for solving the model is also designed, and a large number of experiments and analyses are carried out on multiple data sets to verify the superiority of the model.
- References:
- -
- Similar References:
Memo
-
Last Update:
1900-01-01