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• W2938149915 startingPage "e0209083" @default.
• W2938149915 abstract "An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest due to their fast learning speed, satisfactory generalization ability and ease of implementation. In this paper, we extend this theory to hypercomplex space and attempt to simultaneously consider multisource information using a hypercomplex representation. To illustrate the performance of the proposed hypercomplex extreme learning machine (HELM), we have applied this scheme to the task of multispectral palmprint recognition. Images from different spectral bands are utilized to construct the hypercomplex space. Extensive experiments conducted on the PolyU and CASIA multispectral databases demonstrate that the HELM scheme can achieve competitive results. The source code together with datasets involved in this paper can be available for free download at https://figshare.com/s/01aef7d48840afab9d6d." @default.
• W2938149915 created "2019-04-25" @default.
• W2938149915 creator A5015488490 @default.
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• W2938149915 date "2019-04-15" @default.
• W2938149915 modified "2023-09-24" @default.
• W2938149915 title "Hypercomplex extreme learning machine with its application in multispectral palmprint recognition" @default.
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• W2938149915 doi "https://doi.org/10.1371/journal.pone.0209083" @default.
• W2938149915 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6464184" @default.
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