.png){kind=icon}
.png){kind=icon}
Return
Multiple kernel dimensionality reduction based on linear regression virtual reconstruction for image set classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2019.06.066.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose a novel multiple kernel dimensionality reduction (DR) method based on linear regression reconstruction mechanism for image set based classification. The proposed method aims to learn an optimal kernel automatically from the multiple base kernels and a projection transformation such that in the projected low dimensional subspace, the between-class reconstruction error denoted by the distance of the between-class reconstructed virtual samples is maximized and the within-class reconstruction error denoted by the distance of the within-class reconstructed virtual samples is minimized. Therefore, the compactness of reconstructed within-class virtual samples is enhanced, and between-class reconstructed virtual samples are better separated. This feature extraction scheme can best work with the corresponding classification strategy, which will naturally enhance the classification performance. By employing the method of trace ratio maximization, we also develop a framework to solve the resulting nonconvex optimization problem efficiently. Extensive experiments on benchmark image set datasets well demonstrate the effectiveness of the proposed method compared with other set based methods. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Image set
Linear regression
Multiple kernel learning
Discriminative projection analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
No organization information available