Abstract
The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy. However, it is an expensive job to collect enough labeled training data in the broad outdoor space. In order to reduce the cost of building and maintaining training database, semi-supervised extreme learning machine is applied to solve the cellular network localization in this article. However, the performance of this algorithm is sensitive to the values of the hyper parameters. Without any systematic guidance, the optimal hyper parameters can only be selected by experienced workers through trial and error. To address this problem, we propose a novel algorithm by combining particle swarm optimization and semi-supervised extreme learning machine to automatically select the optimal hyper parameters of semi-supervised extreme learning machine in this article. ...
Highlights
The highly developed cellular network with worldwide range signal and the popularity of the smartphone make the localization technology based on cellular network become an important outdoor localization technology
By comparing the optimization of strategy proposed PSOSSELM with the optimization strategies of E-extreme learning machine (ELM) and particle swarm optimization (PSO)-ELM, we can infer that the most appropriate way to improve the performance of ELM family in the case of lack of labeled data is to give reasonable hyper parameters
The outdoor localization based on received signal strength (RSS) with the cellular network usually need to collect labeled training data
Summary
The highly developed cellular network with worldwide range signal and the popularity of the smartphone make the localization technology based on cellular network become an important outdoor localization technology. Considering the lack of labeled information in semi-supervised learning problem, PSO optimizes the hyper parameters according to both the loss on labeled data and the number of extreme values on the whole dataset (including labeled and unlabeled data) in the framework. This important feature of PSO-SSELM makes the SS-ELM obtain better performance. In order to use the unlabeled data to improve the stability of ELM in the case of lack of labeled training data, Huang et al.[10] put forward a semi-supervised learning extreme learning machine (SSELM) using the manifold framework arg min LSSÀELM b2RN~ 3 m. If the global optimal fitness has met convergence condition, stop the iterations and obtain the optimal hyper parameters, otherwise continue the iterations from Step 3
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