.png){kind=icon}
返回
Tensor Dictionary Learning for Positive Definite Matrices
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2015.2440766.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sparse models have proven to be extremely successful in image processing and computer vision. However, a majority of the effort has been focused on sparse representation of vectors and low-rank models for general matrices. The success of sparse modeling, along with popularity of region covariances, has inspired the development of sparse coding approaches for these positive definite descriptors. While in earlier work, the dictionary was formed from all, or a random subset of, the training signals, it is clearly advantageous to learn a concise dictionary from the entire training set. In this paper, we propose a novel approach for dictionary learning over positive definite matrices. The dictionary is learned by alternating minimization between sparse coding and dictionary update stages, and different atom update methods are described. A discriminative version of the dictionary learning approach is also proposed, which simultaneously learns dictionaries for different classes in classification or clustering. Experimental results demonstrate the advantage of learning dictionaries from data both from reconstruction and classification viewpoints. Finally, a software library is presented comprising C++ binaries for all the positive definite sparse coding and dictionary learning approaches presented here.
Keyword:
Sparse coding
dictionary learning
positive definite matrices
region covariance descriptors
optimization
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:
13.7
论文数:
1.0W
被引数:
8.4W
机构
3
引用论文
分享收藏A micromachined efficient parametric array loudspeaker with a wide radiation frequency band具有宽辐射频带的微机械高效参量阵列扬声器
PREAI
分享收藏
Equilibrium and kinetic mechanisms of woody biochar on aqueous glyphosate removal.png)
Chemosphere
ChemosphereIF0
PREAI
分享收藏
查看更多内容