ONLINE SPARSE SUBSPACE CLUSTERING

被引：0

作者：

Madden, Liam ^{[1
]}

Becker, Stephen ^{[1
]}

Dall'Anese, Emiliano ^{[2
]}

机构：

[1] Univ Colorado Boulder, Dept Appl Math, Boulder, CO 80309 USA

[2] Univ Colorado Boulder, Dept Elect Comp & Energy Engn, Boulder, CO USA

来源：

2019 IEEE DATA SCIENCE WORKSHOP (DSW) | 2019年

关键词：

Subspace clustering; sparse representation; time-varying optimization; online algorithms; OPTIMIZATION;

D O I：

10.1109/dsw.2019.8755556

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper focuses on the sparse subspace clustering problem, and develops an online algorithmic solution to cluster data points on-the-fly, without revisiting the whole dataset. The strategy involves an online solution of a sparse representation (SR) problem to build a (sparse) dictionary of similarities where points in the same subspace are considered "similar," followed by a spectral clustering based on the obtained similarity matrix. When the SR cost is strongly convex, the online solution converges to within a neighborhood of the optimal time-varying batch solution. A dynamic regret analysis is performed when the SR cost is not strongly convex.

引用

页码：248 / 252

页数：5

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