.png){kind=icon}
.png){kind=icon}
Return
Discriminant structure embedding for image recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.09.071.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Neighborhood preserving embedding (NPE) has been widely used to learn the intrinsic structure of data. However, it may impair the local topology and ignore the diversity of data. In this paper, we present a dimensionality reduction approach, namely discriminant neighborhood structure embedding (DNSE). DNSE constructs an adjacency graph to characterize the diversity of data and combines NPE to learn the local intrinsic geometric structure, which well characterizes both similarity and diversity. Finally, the global structure, which is obtained by LDA, is integrated with the aforementioned local structure to build the objective function. Experiments on the four image databases illustrate the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
NPE
Dimensionality reduction
Manifold learning
Face recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.