Abstract
Representational learning using Extreme Learning Machine (ELM) theory has aroused lots of interest. Motivated by recent advance in ELM, this paper presents a novel Extreme Latent Representation (ELR) learning model to seamlessly connect original perception data and the corresponding high-level semantics. Specifically, ELM and ELM based auto-encoder (ELM-AE) are formulated in a unified learning model with both classification and reconstructive ability of the representation considered. ELR inherits the merits of ELM and ELM-AE, and discriminative and compact representation can be learnt with data information well preserved. Furthermore, an efficient algorithm based on alternating direction method of multipliers (ADMM) is developed to solve the resulting ELR model. The performance of ELR is verified on two visual classification tasks, and encouraging results have are achieved.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (No. 61771079, 61571069, 61801072), the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN201800632, KJQN201800617), and Foundation and Frontier Research Project of Chongqing Municipal Science and Technology Commission (No. cstc2018jcyjAX0344, cstc2018jcyjAX0549, cstc2017zdcy-zdzxX0002).
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Guo, T., Zhang, L., Tan, X. (2020). Extreme Latent Representation Learning for Visual Classification. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_8
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DOI: https://doi.org/10.1007/978-3-030-23307-5_8
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