Abstract
L1-norm based dimensionality reduction methods are the most effective techniques in computer vision and pattern recognition. However, they emphasize the robustness to outliers too much and overlook the correlation information among data so that they usually encounter the instability problem. To overcome this problem, in this paper, we propose a method called L1-norm and trace Lasso based locality correlation projection (L1/TL-LRP), in which the robustness, sparsity, and correlation are jointly considered. Specifically, by introducing the trace Lasso regularization, L1/TL-LRP is adaptive to the correlation structure that benefits from both L2-norm and L1-norm. Besides, an effective procedure based on Alternating Direction Method of Multipliers is proposed for solving L1/TL-LRP. Finally, we conduct extensive experiments on several databases for data classification. The inspiring experimental results demonstrate the effectiveness of the proposed method.
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Yuan, S., Chen, S., Zhang, F., Huang, W. (2021). L1-Norm and Trace Lasso Based Locality Correlation Projection. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_6
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DOI: https://doi.org/10.1007/978-981-16-2336-3_6
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