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• W4289341942 abstract "Learning an efficient discriminative projection to extract meaningful features from high-dimensional data is a challenging task in machine learning. However, most of the existing discriminative projection literatures have the following two issues: (1) the performance of projection would largely degenerate when handling the data corrupted by complex outlier, e.g., noisy and outlier mixed data; and (2) fixed graph in graph regularization can be negatively affected by outliers or noisy features. To deal with above issues, we design a new feature extraction method called robust discriminative projection with dynamic graph regularization (RDPDG) to handle above problems. Specifically, RDPDG simulates complex outlier features, i.e., low rank and sparse structured outlier features, using two matrices constrained by the nuclear and adaptive loss norm. RDPDG is able to boost the performance of classification, which primarily due to two key factors: (1) learns reliable projection through a unified framework that integrates discriminant analysis, reconstituted data and manifold learning; and (2) can exploit the local intrinsic structure among data effectively by dynamic graph regularization. Furthermore, an alternating iteration algorithm is designed to solve the proposed nonconvex model, which seamlessly connects the update process of all components in the RDPDG framework, offering a powerful discriminative capability for projection. Comprehensive experiments on a synthetic and several real-world data sets demonstrate that the RDPDG outperforms the precious related methods for classification." @default.
• W4289341942 created "2022-08-02" @default.
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• W4289341942 date "2022-10-01" @default.
• W4289341942 modified "2023-10-16" @default.
• W4289341942 title "Robust discriminative projection with dynamic graph regularization for feature extraction and classification" @default.
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• W4289341942 doi "https://doi.org/10.1016/j.knosys.2022.109563" @default.
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