caa a22 4500 606160353 CHVBK 20210128100629.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00521-014-1718-6 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1718-6 Discriminative structure discovery via dimensionality reduction for facial image manifold [Elektronische Daten] [Xiaoming Chen, Ke Fan, Wanquan Liu, Xin Zhang, Mingliang Xue] Manifold learning aims to map the original data from a high-dimensional space into a low-dimensional feature space with possible better discriminative structure. In this paper, we propose a supervised manifold learning approach called SubManifold Individuality LEarning (SMILE). In SMILE, the linear subspace derived from the principal component analysis based on data with the same label is named as "the individual subspace”, while the linear subspace learned from all data is defined as "the global subspace”. For each data sample, the aim of SMILE is to enlarge the diversity between its reconstructed data from individual subspace and global subspace, respectively, so that the intrinsic character of each class can be stimulated in the feature space. SMILE also utilizes the Laplacian matrix to restrict the local structure of data in the low-dimensional feature space in order to preserve the locality of the high-dimensional data. The proposed method is validated in appearance-based face recognition problem on some typical facial image databases via extracting discriminative features. Experimental results show that the proposed approach can obtain the discriminative structure of facial manifold and extract better features for face recognition than other counterparts approaches. The Natural Computing Applications Forum, 2014 Manifold learning nationallicence Feature extraction nationallicence Face recognition nationallicence Subspace learning nationallicence Chen Xiaoming Digital Technology International Group, 6105, Perth Airport, WA, Australia aut Fan Ke Department of Computing, Curtin University, 6102, Bentley, WA, Australia aut Liu Wanquan Department of Computing, Curtin University, 6102, Bentley, WA, Australia aut Zhang Xin Department of Computing, Curtin University, 6102, Bentley, WA, Australia aut Xue Mingliang Department of Computing, Curtin University, 6102, Bentley, WA, Australia aut Neural Computing and Applications Springer London 26/2(2015-02-01), 373-381 0941-0643 26:2<373 2015 26 521 https://doi.org/10.1007/s00521-014-1718-6 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/s00521-014-1718-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Xiaoming Digital Technology International Group, 6105, Perth Airport, WA, Australia aut NATIONALLICENCE 700 1- Fan Ke Department of Computing, Curtin University, 6102, Bentley, WA, Australia aut NATIONALLICENCE 700 1- Liu Wanquan Department of Computing, Curtin University, 6102, Bentley, WA, Australia aut NATIONALLICENCE 700 1- Zhang Xin Department of Computing, Curtin University, 6102, Bentley, WA, Australia aut NATIONALLICENCE 700 1- Xue Mingliang Department of Computing, Curtin University, 6102, Bentley, WA, Australia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/2(2015-02-01), 373-381 0941-0643 26:2<373 2015 26 521