.png){kind=icon}
Return
Max-Margin-Based Discriminative Feature Learning
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2016.2520099.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this brief, we propose a new max-margin-based discriminative feature learning method. In particular, we aim at learning a low-dimensional feature representation, so as to maximize the global margin of the data and make the samples from the same class as close as possible. In order to enhance the robustness to noise, we leverage a regularization term to make the transformation matrix sparse in rows. In addition, we further learn and leverage the correlations among multiple categories for assisting in learning discriminative features. The experimental results demonstrate the power of the proposed method against the related state-of-the-art methods.
Keywords:
Correlation relationship
feature learning
max-margin
row sparsity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
7.6K
Citations:
7.2W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more