Regularized coplanar discriminant analysis for dimensionality reduction. (February 2017)

• Record Type: Journal Article
• Authors: Huang, Ke-Kun; Dai, Dao-Qing; Ren, Chuan-Xian
• Is Part Of: Pattern recognition. Volume 62(2017:Feb.)
• Page Start: 87
• Page End: 98
• Publication Date: 2017-02
• Subjects: Dimensionality reduction -- Sparse representation classifier -- Face recognition -- Hyperspectral image classification -- Coplanar discriminant analysis; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.08.024
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: The dimensionality reduction methods based on linear embedding, such as neighborhood preserving embedding (NPE), sparsity preserving projections (SPP) and collaborative representation based projections (CRP), try to preserve a certain kind of linear representation for each sample after projection. However, in the transformed low-dimensional space, the linear relationship between the samples may be changed, which cannot make the linear representation-based classifiers, such as sparse representation-based classifier (SRC), to achieve higher recognition accuracy. In this paper, we propose a new linear dimensionality reduction algorithm, called Regularized Coplanar Discriminant Analysis (RCDA) to address this problem. It simultaneously seeks a linear projection matrix and some linear representation coefficients that make the samples from the same class coplanar and the samples from different classes not coplanar. The proposed regularization term balances the bias from the optimal linear representation and that from the class mean to avoid overfitting the training data, and overcomes the matrix singularity in solving the linear representation coefficients. An alternative optimization approach is proposed to solve the RCDA model. Experiments are done on several benchmark face databases and hyperspectral image databases, and results show that RCDA can obtain better performance than other dimensionality reduction methods. Abstract : Highlights: RCDA simultaneously finds a projection matrix and linear representation coefficients. RCDA makes the samples from the same class coplanar. The linear representation coefficients are regularized by the proposed mean L2 norm. An optimization algorithm is proposed to solve the model of RCDA.