Name
Title | ||
|---|---|---|
A Trace Lasso Regularized L1-norm Graph Cut for Highly Correlated Noisy Hyperspectral Image. |
Abstract | ||
|---|---|---|
This work proposes an adaptive trace lasso regularized L1-norm based graph cut method for dimensionality reduction of Hyperspectral images, called as 'Trace Lasso-L1 Graph Cut' (TL-L1GC). The underlying idea of this method is to generate the optimal projection matrix by considering both the sparsity as well as the correlation of the data samples. The conventional L2-norm used in the objective function is sensitive to noise and outliers. Therefore, in this work L1-norm is utilized as a robust alternative to L2-norm. Besides, for further improvement of the results, we use a penalty function of trace lasso with the L1GC method. It adaptively balances the L2-norm and L1-norm simultaneously by considering the data correlation along with the sparsity. We obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1-norm with trace lasso as the penalty. Furthermore, an iterative procedure for this TL-L1GC method is proposed to solve the optimization function. The effectiveness of this proposed method is evaluated on two benchmark HSI datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/EUSIPCO.2018.8553383 | European Signal Processing Conference |
Keywords | DocType | Volume |
Correlation,dimensionality reduction,graph cut,greedy method,hyperspectral classification,L1-norm,sparsity,trace lasso | Conference | abs/1807.10602 |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramanarayan Mohanty | 1 | 2 | 2.39 |
| S. L. Happy | 2 | 51 | 9.11 |
| Nilesh Suthar | 3 | 0 | 0.34 |
| Aurobinda Routray | 4 | 337 | 52.80 |