Abstract
Multiobjective sparse reconstruction (MOSR) methods can potentially obtain superior reconstruction performance. However, they suffer from high computational cost, especially in high-dimensional reconstruction. Furthermore, they are generally implemented independently without reusing prior knowledge from past experiences, leading to unnecessary computational consumption due to the re-exploration of similar search spaces. To address these problems, we propose a sparse-constraint knowledge transfer operator to accelerate the convergence of MOSR solvers by reusing the knowledge from past problem-solving experiences. Firstly, we introduce the deep nonlinear feature coding method to extract the feature mapping between the search of the current problem and a previously solved MOSR problem. Through this mapping, we learn a set of knowledge-induced solutions which contain the search experience of the past problem. Thereafter, we develop and apply a sparse-constraint strategy to refine these learned solutions to guarantee their sparse characteristics. Finally, we inject the refined solutions into the iteration of the current problem to facilitate the convergence. To validate the efficiency of the proposed operator, comprehensive studies on extensive simulated signal reconstruction are conducted.
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Bader, J., Zitzler, E.: HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)
Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)
Chen, M., Xu, Z., Weinberger, K., Sha, F.: Marginalized denoising autoencoders for domain adaptation. In: Proceedings of the 29th International Conference on Machine Learning (2012)
Combettes, P.L., Wajs, V.R.: Signal recovery by proximal forward-backward splitting. Multiscale Model. Simul. 4(4), 1168–1200 (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Dong, Z., Zhu, W.: Homotopy methods based on \(l_0\)-norm for compressed sensing. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 1132–1146 (2018)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Feng, L., Ong, Y.S., Jiang, S., Gupta, A.: Autoencoding evolutionary search with learning across heterogeneous problems. IEEE Trans. Evol. Comput. 21(5), 760–772 (2017)
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J.: A kernel method for the two-sample-problem. In: Proceedings of the Conference on Neural Information Processing System, pp. 513–520 (2007)
Gupta, A., Ong, Y.S., Feng, L.: Insights on transfer optimization: because experience is the best teacher. IEEE Trans. Emerg. Topics Comput. Intell. 2(1), 51–64 (2018)
Jiang, M., Huang, Z., Liming, Q., Huang, W., et al.: Transfer learning based dynamic multiobjective optimization algorithms. IEEE Trans. Evol. Comput. 22(4), 501–514 (2017)
Jiao, Y., Jin, B., Lu, X.: Iterative soft/hard thresholding with homotopy continuation for sparse recovery. IEEE Signal Process. Lett. 24(6), 784–788 (2017)
Li, H., Zhang, Q., Deng, J., Xu, Z.B.: A preference-based multiobjective evolutionary approach for sparse optimization. IEEE Trans. Neural Netw. Learn. Syst. 29(5), 1716–1731 (2018)
Li, L., Yao, X., Stolkin, R., Gong, M., He, S.: An evolutionary multiobjective approach to sparse reconstruction. IEEE Trans. Evol. Comput. 18(6), 827–845 (2014)
Liu, C., Zhao, Q., Yan, B., Elsayed, S., Ray, T., Sarker, R.: Adaptive sorting-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. (in press). https://doi.org/10.1109/TEVC20182848254
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)
Sentelle, C.G., Anagnostopoulos, G.C., Georgiopoulos, M.: A simple method for solving the SVM regularization path for semidefinite kernels. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 709–722 (2016)
Steinwart, I.: On the influence of the kernel on the consistency of support vector machines. J. Mach. Learn. Res. 2(Nov), 67–93 (2001)
Wei, P., Ke, Y., Goh, C.K.: Deep nonlinear feature coding for unsupervised domain adaptation. In: IJCAI, pp. 2189–2195 (2016)
Yan, B., Zhao, Q., Wang, Z., Zhang, J.A.: Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction. Inf. Sci. 462, 141–159 (2018)
Yan, B., Zhao, Q., Wang, Z., Zhao, X.: A hybrid evolutionary algorithm for multiobjective sparse reconstruction. Signal Image Video P. 11, 993–1000 (2017)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Zhou, Y., Kwong, S., Guo, H., Zhang, X., Zhang, Q.: A two-phase evolutionary approach for compressive sensing reconstruction. IEEE Trans. Cybern. 47(9), 2651–2663 (2017)
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This work was supported by the China Scholarship Council under Grant 201706540025.
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Yan, B., Zhao, Q., Zhang, J.A., Li, Y., Wang, Z. (2019). Convergence Acceleration for Multiobjective Sparse Reconstruction via Knowledge Transfer. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_38
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DOI: https://doi.org/10.1007/978-3-030-12598-1_38
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