Unsupervised double weight graphs based discriminant analysis for dimensionality reduction

Dimensionality reduction plays an important role in pattern recognition tasks. Locality preserving projection and neighbourhood preserving embedding are popular unsupervised feature extraction methods, which try to preserve a certain local structure in the low-dimensional subspace. However, only considering the local neighbour information will limit the methods to achieve higher recognition accuracy. In this paper, an unsupervised double weight graphs based discriminant analysis method (uDWG-DA) is proposed. First, uDWG-DA considers both similar and dissimilar relationships among samples by using double weight graphs. In order to explore the dissimilar information, a new partitioning strategy is proposed to divide the data set into different clusters, where samples of different clusters are dissimilar. Then, based on L-2,L-1 norm, uDWG-DA finds the optimal projection to not only preserve the similar local structure but also increase the separability among different clusters of the data set. Experiments on four hyperspectral images validate the advantage and feasibility of the proposed method compared with other dimensionality reduction methods.