A global manifold margin learning method for data feature extraction and classification. (October 2018)

• Record Type: Journal Article
• Authors: Li, Bo; Guo, Wei; Zhang, Xiao-Long
• Is Part Of: Engineering applications of artificial intelligence. Volume 75(2018)
• Page Start: 94
• Page End: 101
• Publication Date: 2018-10
• Subjects: Feature extraction -- Supervised manifold learning -- Manifold margin; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.08.004
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents a global manifold margin learning approach for data feature extraction or dimensionality reduction, which is named locally linear representation manifold margin (LLRMM). Provided that points locating on one manifold are of the same class and those residing on the corresponding manifolds are varied labeled, LLRMM is desired to identify different manifolds, respectively. In the proposed LLRMM, it firstly constructs both a between-manifold graph and a within-manifold graph. In the between-manifold graph, for any point, its k nearest neighbors and itself must belong to different manifolds. However, any node and its neighborhood points should be on the same manifold in the within-manifold graph. Then we use the minimum locally linear representation trick to reconstruct any node with their corresponding k nearest neighbors in both graphs, from which a between-manifold graph scatter and a within-manifold graph scatter can be reasoned, followed by a novel global model of manifold margin. At last, a projection will be explored to map the original data into a low dimensional subspace with the maximum manifold margin. Experiments on some widely used face data sets including AR, CMU PIE, Yale, YaleB and LFW have been carried out, where the performance of the proposed LLRMM outperforms those of some other methods such as kernel principal component analysis (KPCA), non-parametric discriminant analysis (NDA), reconstructive discriminant analysis (RDA), discriminant multiple manifold learning (DMML) and large margin nearest neighbor (LMNN). Highlights: A novel manifold margin is globally defined with the minimum locally linear representation trick. Based on the manifold margin, a LLRMM method is presented for multi-manifold identification. LLRMM attempts to explore a discriminant subspace to maximize the manifold margin. The proposed method outperforms some related methods on benchmark face data.