Name
Abstract | ||
|---|---|---|
In this paper, we propose an L1 normalized graph based dimensionality reduction method for Hyperspectral images, called as 'L1-Scaling Cut' (L1-SC). The underlying idea of this method is to generate the optimal projection matrix by retaining the original distribution of the data. Though L2-norm is generally preferred for computation, it is sensitive to noise and outliers. However, L1-norm is robust to them. Therefore, we obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1-norm. Furthermore, an iterative algorithm is described to solve the optimization problem. The experimental results of the HSI classification confirm the effectiveness of the proposed L1-SC method on both noisy and noiseless data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.23919/EUSIPCO.2017.8081316 | European Signal Processing Conference |
Keywords | DocType | Volume |
Dimensionality reduction,Hyperspectral classification,L1-norm,L1-SC,scaling cut,Supervised learning | Journal | abs/1709.02920 |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramanarayan Mohanty | 1 | 2 | 2.39 |
| S. L. Happy | 2 | 51 | 9.11 |
| Aurobinda Routray | 3 | 337 | 52.80 |