摘要
传统谱聚类算法直接对原始数据建立高斯核邻接矩阵后再对数据进行聚类,并未考虑数据的深层次特征以及数据的邻域流形结构,并且仅进行单一聚类,针对以上三点不足,提出了利用稀疏自编码的局部谱聚类映射算法(LSCMS),通过对数据进行预处理,利用稀疏自编码提取能反映原始数据本质的深层次特征,并以此替代原始数据;对每个数据利用其邻域进行线性重构,以重构权值代替高斯核函数建立邻接矩阵。LSCMS在聚类同时将数据映射到聚类指标上进而协调聚类指标。在UCI数据集、手写数据集、人脸数据集上的实验结果表明:算法优于现有的聚类算法。
Traditional spectral clustering algorithms establish direct adjacency matrix using Gaussian kernel,and then do original data clustering,without taking into account deep feature of the data as well as the manifold structure of the neighborhood,but only carry out single cluster,in view of the above three shortcomings,put forward a local spectral clustering and mapping algorithm using sparse autoencoders（LSCMS）. Through data preprocessing,LSCMS uses sparse auto-coding to extract deep characteristics of the original data set,which can better reflect the characteristics of the sample,so as to replace the original data; and reconstructs adjacency matrix by its linear neighborhood instead of Gaussian kernel function. LSCMS clusters and maps data to cluster index simultaneously so as to coordinate the cluster indicator. Experimental results on UCI datasets,handwritten datasets,face datasets show that the algorithm is superior to the existing clustering algorithms.
出处
《传感器与微系统》
CSCD
2018年第1期145-148,153,共5页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61373055)
江苏省2015年度普通高校研究生科研创新计划资助项目(KYLX15_1191)
关键词
稀疏自编码
谱聚类
映射
深度学习
线性邻域
sparse autoencoders
spectral clustering
mapping
deep learning
linear neighborhood
