.png){kind=icon}
返回
wSparse Coefficient-Based k-Nearest Neighbor Classification
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2017.2739807.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
K-nearest neighbor rule (KNN) and sparse representation (SR) are widely used algorithms in pattern classification. In this paper, we propose two new nearest neighbor classification methods, in which the novel weighted voting methods are developed for making classification decisions on the basis of sparse coefficients in the SR. Since the sparse coefficients can well reflect the neighborhood structure of data, we mainly utilize them to design classifier in the proposed methods. One proposed method is called the coefficient-weighted KNN classifier, which adopts sparse coefficients to choose KNNs of a query sample and then uses the coefficients corresponding to the chosen neighbors as their weights for classification. Another new method is the residual-weighted KNN classifier (RWKNN). In the RWKNN, KNNs of a query sample are first determined by sparse coeficients, and then, we design a novel residual-based weighted voting method for the KNN classification. The extensive experiments are carried out on many UCI and KEEL data sets, and the experimental results show that the proposed methods perform well.
Keyword:
K-nearest neighbor rule
sparse coefficient
weighted voting
pattern classification
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}
分享收藏Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification基于稀疏表示的Fisher判别字典学习图像分类
PREAI
分享收藏
查看更多内容