Details

Autor(en) / Beteiligte

• Guosheng Liu
• Meizhu Yang

Titel

Discriminative locality preserving dimensionality reduction based on must-link constraints

Ist Teil von

• Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 2011, Vol.7, p.3413-3417

Ort / Verlag

IEEE

Erscheinungsjahr

2011

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Locality preserving manifold learning algorithms are seeking intrinsic manifold based on overlapping local geometry structure. Locality Preserving Projections (LPP) and Neighborhood Preserving Embedding (NPE) are two representative linear locality preserving manifold learning algorithms, which not only defined on training samples, but also can generalize to test samples. But they just take the local structure into consideration, ignoring some available prior information. Pairwise constraints are easier obtained supervised information compared with labels. In this paper, we proposed two discriminative locality preserving manifold learning algorithms, by incorporating must-link constraints into LPP and NPE to improve their discriminative ability. Experiments results on Yale and ORL face databases verified the effectiveness.