Abstract
In this paper, we propose a novel feature selection method to jointly map original data to kernel space and conduct both subspace learning (via locality preserving projection) and feature selection (via a sparsity constraint). The kernel method is used to explore the nonlinear relationship between data and subspace learning is used to maintain the local structure of the data. As a result, we eliminate redundant and irrelevant features and thus make our method select a large amount of informative and distinguishing features. By comparing our proposed method with some state-of-the-art methods, the experimental results showed that the proposed method outperformed the comparisons in terms of clustering task.
Keywords
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Chen, L., Zhong, Z. (2018). Joint Subspace Learning and Sparse Regression for Feature Selection in Kernel Space. In: Chen, Q., Wu, J., Zhang, S., Yuan, C., Batten, L., Li, G. (eds) Applications and Techniques in Information Security. ATIS 2018. Communications in Computer and Information Science, vol 950. Springer, Singapore. https://doi.org/10.1007/978-981-13-2907-4_13
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DOI: https://doi.org/10.1007/978-981-13-2907-4_13
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