Name
Title | ||
|---|---|---|
Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization. |
Abstract | ||
|---|---|---|
Wi-Fi-based indoor localization with high capability and feasibility needs to implement lifelong online learning mechanism. However, the characteristic of Wi-Fi is wide variability, which lies in not only the fluctuation of signal strength value, but also the increase or decrease in the number of access points (APs). The traditional algorithms are effective for signal fluctuation, but cannot handle the dimension-changing problem of features caused by increase and decrease in APs’ number. To solve this problem, we propose a Feature Adaptive Online Sequential Extreme Learning Machine (FA-OSELM) algorithm. It can transfer the original model to a new one with a small number of data with new features, so as to make the new model suitable for the new feature dimension. The experiments show that the FA-OSELM can get higher accuracy with a small amount of new data, and it is an effective method to make lifelong indoor localization practical. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00521-014-1714-x | Neural Computing and Applications |
Keywords | Field | DocType |
Feature adaptive, Online Sequential Extreme Learning Machine (OS-ELM), Lifelong, Indoor localization | Small number,Online learning,Extreme learning machine,Effective method,Online sequential,Signal strength,Artificial intelligence,Mathematics,Machine learning,Feature Dimension | Journal |
Volume | Issue | ISSN |
27 | 1 | 1433-3058 |
Citations | PageRank | References |
9 | 0.58 | 24 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinlong Jiang | 1 | 76 | 10.70 |
| Junfa Liu | 2 | 357 | 26.85 |
| Yiqiang Chen | 3 | 1446 | 109.32 |
| Dingjun Liu | 4 | 9 | 0.58 |
| Yang Gu | 5 | 56 | 7.54 |
| Zhenyu Chen | 6 | 470 | 25.35 |