MULTILINEAR SUBSPACE CLUSTERING

被引：0
|
作者：
Kernfeld, Eric [1 ]
Majumder, Nathan [2 ]
Aeron, Shuchin [2 ]
Kilmer, Misha [2 ]
机构：
[1] Univ Washington, Seattle, WA 98195 USA
[2] Tufts Univ, Medford, MA 02155 USA
来源：
2016 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2016年
关键词：
subspace clustering; multilinear algebra; spectral clustering;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper we present a new model and an algorithm for unsupervised clustering of 2-D data such as images. We assume that the data comes from a union of multilinear subspaces (UOMS) model, which is a specific structured case of the much studied union of subspaces (UOS) model. For segmentation under this model, we develop Multilinear Subspace Clustering (MSC) algorithm and evaluate its performance on the YaleB and Olivietti image data sets. We show that MSC is highly competitive with existing algorithms employing the UOS model in terms of clustering performance while enjoying improvement in the computational complexity.
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页数：5
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