A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

<div>Effect of different number of remaining eigenvectors <svg height="8.68572pt" id="M85" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.13765 8.68572" width="8.13765pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M593 650H142L136 622C219 616 224 611 209 531L130 120C114 39 108 34 23 28L17 0H501C518 32 557 130 568 158L540 169C519 132 495 93 469 70C439 43 409 35 332 35C272 35 241 37 225 50C211 61 211 89 222 149L253 320H350C441 320 443 312 441 239H471L510 434H481C455 366 448 358 360 358H260L303 572C311 613 312 615 348 615H431C509 615 522 603 532 581C544 555 546 531 545 491L574 493C583 555 592 631 593 650Z" id="g113-70"></path></g></svg> on the mean location error.</div>

Wireless Communications and Mobile Computing

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Figure 3

Figure 3: A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things 