Maximum Correntropy Criterion-Based Low-Rank Preserving Projection for Hyperspectral Image Classification

In this letter, we propose a maximum correntropy criterion-based low-rank preserving projection (MCC-LRPP) for hyperspectral image (HSI) classification, seeking a low-dimensional subspace via low-rank correntropy graph where spectral band structure can be preserved as much as possible. Unlike the sparse and low-rank-based techniques available, MCC-LRPP introduces maximum correntropy criteria (MCC) to model individual band reconstruction error and noise discriminately instead of l(2) and Frobenius related norms. It is equivalent to a row-weighting regularization problem. It puts more emphasis on bands with less noise and indirectly increase their importance and vice versa. MCC-LRPP enhances band difference and thus preserves their local structure as well as global structure. Indeed, more local structure means more discriminant ability. The experimental results on several popular HSI data sets prove its effectiveness and superiority when compared to other existing dimension reduction means.