Abstract
Multi-view feature selection is an important research direction in multi-view learning. Most of the existing multi-view feature selection methods focus on the correlation between features and data category structure, while ignoring the redundancy between features. In this paper, we propose a multi-view feature selection method based on low redundancy learning, which introduces and automatically assigns the weight of feature redundancy in each view to the projection space matrix. Subsequently, by applying \(l_{2,1}\) norm to the projection space matrix to constrain row sparsity, the feature subsets with high correlation and low redundancy can be selected. In order to make full use of the consistency of multiple views, we also utilize spectral analysis to learn the potential category structure of each view, and minimize the difference between single-view category structure and consensus clustering indication matrix. Finally, an alternating iterative updating method is presented to solve the optimization problem. Experiments on different public multi-view data sets verify the effectiveness of the proposed method.
This work was supported by the National Natural Science Foundation of China under Grant 61806131 and the Natural Science Foundation of Guangdong Province under Grant 2018A030310510.
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Jia, H., Huang, J. (2023). Low Redundancy Learning for Unsupervised Multi-view Feature Selection. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_16
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