A robust sparse feature selection for hyperspectral images

The hyperspectral imaging system is a powerful tool in the field of remote sensing. The extremely high dimensionality of these data sets, make the analysis of HSIs a complicated task. So for good learning, it is important to select some important features. Recently in [7] a sparse feature selection method based on regularized regression model proposed to select heterogeneous important features. But in application due to imaging devices, our data are contaminated with the noise. This noise affects the learning process especially for high-dimensional data analysis such as hyperspectral images. In this paper, we propose a robust feature selection method based on the proposed method in [7]. Here our model leads to total least square problem. Experimental results, confirm the performance of the proposed method.