title: Discriminative orthogonal neighborhood-preserving projections for classification.
abstract: Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China. z.tianhao@gmail.com
authors.forename: Tianhao
authors.initials: T
authors.lastname: Zhang
authors.forename: Kaiqi
authors.initials: K
authors.lastname: Huang
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.forename: Dacheng
authors.initials: D
authors.lastname: Tao