Feature Fusion Using Locally Linear Embedding for Classification
Bingyu Sun, Xiao-Ming Zhang, Jiuyong Li, Xuemin Mao · IEEE Transactions on Neural Networks · 2009
In most complex classification problems, many types of features have been captured or extracted. Feature fusion is used to combine features for better classification and to reduce data dimensionality. Kernel-based feature fusion methods are very effective for classification, but they do not reduce data dimensionality. In this brief, we propose an effective feature fusion method using locally linear embedding (LLE). The proposed method overcomes the limitations of LLE, which could not handle different types of features and is inefficient for classification. We propose an efficient algorithm to solve the optimization problem in obtaining weights of different features, and design an efficient method for LLE-based classification. In comparison to other kernel-based feature fusion methods, the proposed method fuses features to a significantly lower dimensional feature space with the same discriminant power. We have conducted experiments to demonstrate the effectiveness of the proposed feature fusion method.