A Randomized Approach to Large-Scale Subspace Clustering

被引：0

作者：

Traganitis, Panagiotis A. ^{[1
]}

Giannakis, Georgios B.

机构：

[1] Univ Minnesota, Dept ECE, Minneapolis, MN 55455 USA

来源：

2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS | 2016年

基金：

美国国家科学基金会;

关键词：

Subspace clustering; big data; sketching; random projections; ALGORITHM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Subspace clustering has become a popular tool for clustering high-dimensional non-linearly separable data. However, state-of-the-art subspace clustering algorithms do not scale well as the number of data increases. The present paper puts forth a novel randomized subspace clustering scheme for high-volume data based on random projections. Performance of the proposed method is assessed via numerical tests, and is compared with state-of-the-art subspace clustering and large-scale subspace clustering methods.

引用

页码：1019 / 1023

页数：5