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Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis

Published: 14 March 2023 Publication History

Abstract

The multi-view clustering method based on graph learning has been extensively studied because of its good clustering effect. However, most of the graph learning methods are based on the original data features, which often contain noise and outliers, and using them directly may lead to learning suboptimal graph. To address the above problems, we propose a multi-view graph learning method based on linear discriminant analysis. On the one hand, the manifold structure obtained using the global graph guides the learning of the discriminative projections for each view, and on the other hand, the common graph is learned using the projection data of each view. Then, an efficient solution is proposed by dividing the problem into two sub-problems. Finally, experiments are conducted on some public multi-view datasets to verify the effectiveness of the method.

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  1. Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis

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    ACAI '22: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
    December 2022
    770 pages
    ISBN:9781450398336
    DOI:10.1145/3579654
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    Published: 14 March 2023

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    Author Tags

    1. graph learning
    2. linear discriminant analysis
    3. multi-view clustering

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