Abstract
In this paper, we propose a robust sensor array optimization method based on sparse learning for multi-feature fusion data classification. The proposed approach contains three key characteristics. First, it considers the intrinsic group structure among features by combining an \(\ell _{F,1}\) norm regularizer design and least squares regression framework. Second, in sensor selection, insignificant feature groups can be eliminated by grouped row sparse coefficients generated by the model, while the \(\varepsilon \)-dragging trick is introduced to improve the classification ability. Third, an efficient alternating iteration algorithm is presented to optimize the convex objective function. The results compared with the other classical methods on gas sensor array data sets demonstrate that the proposed method can effectively reduce the number of sensors with higher classification accuracy.
This work is founded by the Natural Science Foundation of China (No. 62171066).
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Zhao, L., Tian, F., Qian, J., Liu, R., Jiang, A. (2022). Robust Sparse Learning Based Sensor Array Optimization for Multi-feature Fusion Classification. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_15
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