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- W4377225242 abstract "Abstract As an important analysis technique of machine learning, clustering has received extensive attention. However, the high-dimensional data make the clustering speed slow. Recent studies show that hashing can achieve efficient clustering. In this paper, a new binary clustering framework – Simultaneous Unsupervised Feature Selection with Adaptive Loss Constraint and Discrete Binary Hashing for Clustering (ALDBC) is proposed. This method based on adaptive loss functions to achieve data dimensionality reduction and low-rank clustering in Hamming space is integrated into a single framework for mutual guidance and dynamic improvement. More specifically, the framework uses an adaptive loss optimization algorithm, the adaptive loss function is located between the L1-norm and the L2-norm, which combines the advantages of the two to enhance the robustness of outliers and reduce the dimension of high-dimensional data at the same time. Then, the sparse binary matrices are decomposed into low-rank matrices in Hamming space to complete image clustering. Finally, the objective function can rapidly converge with the optimization scheme. Experimental results on datasets show that this method can effectively improve the efficiency of clustering. Compared with the state-of-the-art methods, the time efficiency of ALDBC was improved when processing high-dimensional data." @default.
- W4377225242 created "2023-05-23" @default.
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- W4377225242 date "2023-05-23" @default.
- W4377225242 modified "2023-09-29" @default.
- W4377225242 title "Simultaneous unsupervised feature selection with adaptive loss constraint and discrete binary hashing for clustering" @default.
- W4377225242 doi "https://doi.org/10.21203/rs.3.rs-2584071/v1" @default.
- W4377225242 hasPublicationYear "2023" @default.
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