.png){kind=icon}
返回
Class-Specific Sparse Principal Component Analysis for Visual Classification
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3001202.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Extensive research has demonstrated that dictionary learning is active in improving the performance of the representation based classification. However, dictionary learning suffers from lacking an effective dictionary structure that can well tradeoff the reducing reconstruction error and enhancing the representative information. In this paper, we focus on designing capable dictionary learning architecture for the visual classification task with few-shot training samples. First, we propose a class-specific sparse PCA approach to extend the conventional dictionary learning to dictionary pair learning for visual classification. The orthogonal synthesis dictionary ensures fewer reconstruction errors with fixed dictionary size while the sparse analysis dictionary guarantees the representative features. Second, we develop the Alternating Direction Method of Multipliers algorithm to optimize the analysis dictionary to improve computational efficiency. Extensive experiments conducted on multiple image datasets demonstrate that the proposed class-specific PCA method performs favorably against the state-of-art methods on eight widely used image datasets.
Keyword:
Principal component analysis
Dictionaries
Machine learning
Sparse matrices
Visualization
Task analysis
Image reconstruction
Sparse principal component analysis
visual classification
class-specific dictionary learning
class-shared dictionary learning
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:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Copper ferrocyanide loaded track etched membrane: an effective cesium adsorbent负载亚铁氰化铜的径迹蚀刻膜: 一种有效的铯吸附剂
PREAI
分享收藏β-Adrenergic regulation of Sertoli cell adenylyl cyclase: desensitization by homologous hormoneβ-肾上腺素受体对支持细胞腺苷酸环化酶的调节：同源激素介导的脱敏作用
PREAI
分享收藏
查看更多内容