caa a22 4500 606159614 CHVBK 20210128100625.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1768-9 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1768-9 Projection-optimal local Fisher discriminant analysis for feature extraction [Elektronische Daten] [Zhan Wang, Qiuqi Ruan, Gaoyun An] In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. The Natural Computing Applications Forum, 2014 Dimensionality reduction nationallicence Linear discriminant analysis nationallicence Local Fisher discriminant analysis nationallicence Feature extraction nationallicence Wang Zhan Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut Ruan Qiuqi Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut An Gaoyun Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut Neural Computing and Applications Springer London 26/3(2015-04-01), 589-601 0941-0643 26:3<589 2015 26 521 https://doi.org/10.1007/s00521-014-1768-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1768-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Zhan Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Ruan Qiuqi Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- An Gaoyun Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 589-601 0941-0643 26:3<589 2015 26 521