.png){kind=icon}
Return
Multi-label feature selection via robust flexible sparse regularization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2022.109074.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-label feature selection is an efficient technique to deal with the high dimensional multi-label data by selecting the optimal feature subset. Existing researches have demonstrated that l 1-norm and l 2 , 1 -norm are promising roles for multi-label feature selection. However, two important issues are ignored when existing l 1-norm and l 2 , 1-norm based methods select discriminative features for multi-label data. First, l 1-norm can enforce sparsity on each feature across all instances while numerous selected features lack discrimination due to the generated zero weight values. Second, l 2 , 1-norm not only neglects label -specific features but also ignores the redundancy among features. To this end, we design a Robust Flexible Sparse Regularization norm (RFSR), furthermore, proposing a global optimization framework named Ro-bust Flexible Sparse regularized multi-label Feature Selection (RFSFS) based on RFSR. Finally, an efficient alternating multipliers based optimization scheme is developed to iteratively optimize RFSFS. Empirical studies on fifteen benchmark multi-label data sets demonstrate the effectiveness and efficiency of RFSFS.
Keywords:
Multi-label learning
Feature selection
Sparse regularization
Classification
Journal
IF:
7.6
Papers:
1.4W
Citations:
4.5W
Organization
Cited Papers
ShareSaveShareSave
ShareSaveManifold learning with structured subspace for multi-label feature selection.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSaveShareSave
View more