Joint hypergraph learning and sparse regression for feature selection
نویسندگان
چکیده
منابع مشابه
Feature Selection via Joint Embedding Learning and Sparse Regression
The problem of feature selection has aroused considerable research interests in the past few years. Traditional learning based feature selection methods separate embedding learning and feature ranking. In this paper, we introduce a novel unsupervised feature selection approach via Joint Embedding Learning and Sparse Regression (JELSR). Instead of simply employing the graph laplacian for embeddi...
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In many data analysis tasks, one is often confronted with the problem of selecting features from very high dimensional data. Most existing feature selection methods focus on ranking individual features based on a utility criterion, and select the optimal feature set in a greedy manner. However, the feature combinations found in this way do not give optimal classification performance, since they...
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2017
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2016.06.009