Heterogeneous feature structure fusion for classification. (May 2016) - Lin, Guangfeng; Fan, Guoliang; Kang, Xiaobing; Zhang, Erhu; Yu, Liangjiang
Abstract: The key to feature fusion for classification is to take advantage of the discriminative and complementary information from different feature sets, which can be represented as internal (within each feature set) or external structures (across different feature sets). Traditional approaches tend to preserve either internal or external structures via certain feature projection. Some early attempts consider both structures implicitly or indirectly without revealing their relative importance and relevance in feature fusion. We propose a new unsupervised heterogeneous structure fusion (HSF) algorithm that is able to jointly optimize two kinds of structures explicitly and directly via unified feature projection. Specifically, the internal structure is represented based on Locality Preserving Projection (LPP), and the external structure is captured by Canonical Correlation Analysis (CCA). The objective function of HSF combines two feature structures in a closed form which can be optimized alternately via linear programming and eigenvector methods. The HSF solution provides not only the optimal feature projection but also the weights that encode the relative importance between two kinds of feature structures. The experimental results on image classification, face recognition, shape analysis and infrared target recognition demonstrate the effectiveness and efficiency of HSF compared with state-of-the-art methods. Abstract : Graphical abstract: Abstract : Highlights: The external and internal feature structures are jointly optimized for fusion. Two kinds of feature structures are preserved via a unified feature projection. The nonlinear relationship between two kinds of feature structures is revealed. The new feature fusion algorithm shows great promise in four classification problems.