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• W2883457751 abstract "Recently, L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -norm-based robust discriminant feature extraction technique has been attracted much attention in dimensionality reduction and pattern recognition. However, it does not relate to the scatter matrix which well characterizes the geometric structure of data. In this paper, we propose a robust formulation of graph embedding framework for dimensionality reduction. In this robust framework, we use L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> -norm to measure the distance along space dimension and L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -norm to sum overall data points. The proposed robust graph embedding framework retains the traditional framework's desirable properties, such as rotational invariance and well geometric structure, and simultaneously suppresses outliers. Based on this framework, we develop a simple and robust feature extraction method, namely L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2,1</sub> -norm-based discriminant locality preserving projections (L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2,1</sub> -DLPP) and provide an effective iterative algorithm to solve L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2,1</sub> -DLPP. Extensive experiments in artificial data and three popular face databases illustrate the effectiveness of our proposed method." @default.
• W2883457751 created "2018-08-03" @default.
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• W2883457751 date "2018-01-01" @default.
• W2883457751 modified "2023-10-01" @default.
• W2883457751 title "${L_{2,1}}$ -Norm Discriminant Manifold Learning" @default.
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• W2883457751 doi "https://doi.org/10.1109/access.2018.2859299" @default.
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