Name
Abstract | ||
|---|---|---|
While many existing multi-task learning based Wi-Fi location approaches pay more attention on the location performance, they generally neglect determining key access points(APs). In order to reduce maintenance cost in complex indoor environment, a new multi-task learning based Wi-Fi location approach is proposed to find the key APs with enough accuracy. First, we introduce extreme learning machine as basic method to establish a new multi-task learning machine. This machine is based on the assumption that the hypotheses learned from a latent feature space, rather than the original high-dimensional feature space, are similar, in which L1/2-iiorm is utilized to construct L2-1/2-norm to achieve joint feature selection in multi-task scenario. An alternating optimization method is employed to solve this problem, by iteratively optimizing the latent space and key features. Experiments on real-world indoor localization data are conducted, and the results demonstrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889678 | IJCNN |
Keywords | DocType | ISSN |
optimisation,access points,alternating optimization method,learning (artificial intelligence),latent feature space,multitask learning based Wi-Fi location approach,joint feature selection,extreme learning machine,complex indoor environment,AP,original high-dimensional feature space,wireless LAN,L1/2-norm,real-world indoor localization data | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wentao Mao | 1 | 112 | 11.54 |
| Haicheng Wang | 2 | 0 | 0.34 |
| Shangwang Liu | 3 | 1 | 1.02 |