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• W4309675231 abstract "Latent Factor (LF) models are highly effective in representing high-dimensional and incomplete (HDI) matrices. Hessian free (HF) optimization is an efficiency second-order algorithm to minimize the object function in LF models. An HF-based second-order LF model can achieve better accuracy representation results than first-order ones with affordable computational burden. However, its low rank representation ability relies on a more complex training process, multiple hyper-parameters work cooperatively to decide the results. Thus, these hyper-parameters should be turned with care since they are mutually influenced. The heavy hyper-parameters turning work reduces the practicability of a second-order LF model. To address this issue, this study incorporates the principle of particle swarm optimization into the second-order LF model to propose an adaptive second-order LF (ASLF) model. Experimental results on three HDI matrices reveal that ASLF model can be fine-tuned adaptively with acceptable computation burden." @default.
• W4309675231 created "2022-11-29" @default.
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• W4309675231 date "2022-10-09" @default.
• W4309675231 modified "2023-09-29" @default.
• W4309675231 title "An Adaptive Second-order Latent Factor Model via Particle Swarm Optimization" @default.
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• W4309675231 doi "https://doi.org/10.1109/smc53654.2022.9945329" @default.
• W4309675231 hasPublicationYear "2022" @default.
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