.png){kind=icon}
返回
Max-Margin-Based Discriminative Feature Learning
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2016.2520099.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Correlation relationship
feature learning
max-margin
row sparsity
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
.png){kind=icon}
Classifying a high resolution image of an urban area using super-object information使用超对象信息对城市区域的高分辨率图像进行分类
PREAI
分享收藏分享收藏
查看更多内容