.png){kind=icon}
返回
Fuzzy Double C-Means Clustering Based on Sparse Self-Representation
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2017.2686804.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces the popular sparse representation method into the classical fuzzy c-means clustering algorithm, and presents a novel fuzzy clustering algorithm, called fuzzy double c-means based on sparse self-representation ( FDCM_ SSR). The major characteristic of FDCM_ SSR is that it can simultaneously address two datasets with different dimensions, and has two kinds of corresponding cluster centers. The first one is the basic feature set that represents the basic physical property of each sample itself. The second one is learned from the basic feature set by solving a spare self-representation model, referred to as discriminant feature set, which reflects the global structure of the sample set. The spare self-representation model employs dataset itself as dictionary of sparse representation. It has good category distinguishing ability, noise robustness, and data-adaptiveness, which enhance the clustering and generalization performance of FDCM_ SSR. Experiments on different datasets and images show that FDCM_ SSR is more competitive than other state-of-the- art fuzzy clustering algorithms.
Keyword:
Clustering
fuzzy c-means
sparse representation (SR)
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:
11.9
论文数:
5.0K
被引数:
2.9W
机构
引用论文
.png){kind=icon}
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
查看更多内容