Trace Ratio Criterion Based Large Margin Subspace Learning for Feature Selection
نویسندگان
چکیده
منابع مشابه
Large Margin Subspace Learning for feature selection
Recent research has shown the benefits of large margin framework for feature selection. In this paper, we propose a novel feature selection algorithm, termed as Large Margin Subspace Learning (LMSL), which seeks a projection matrix to maximize the margin of a given sample, defined as the distance between the nearest missing (the nearest neighbor with the different label) and the nearest hit (th...
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Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a feature subset is selected based on the corresponding score (subset-level score), which is calculated in a trace ratio form. Since the number of all possible feature subsets is very huge, it is often prohibitively expens...
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Feature selection is an effective method to deal with high-dimensional data. While in many applications such as multimedia and web mining, the data are often high-dimensional and very large scale, but the labeled data are often very limited. On these kind of applications, it is important that the feature selection algorithm is efficient and can explore labeled data and unlabeled data simultaneo...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2018.2888924