NDLI: Fast algorithm for large-scale subspace clustering by LRR

Content Provider	IET Digital Library
Author	Xie, Deyan Nie, Feiping Gao, Quanxue Xiao, Song
Abstract	Low-rank representation (LRR) and its variants have been proved to be powerful tools for handling subspace clustering problems. Most of these methods involve a sub-problem of computing the singular value decomposition of an n × n matrix, which leads to a computation complexity of O ( n 3 ) . Obviously, when n is large, it will be time consuming. To address this problem, the authors introduce a fast solution, which reformulates the large-scale problem to an equal form with smaller size. Thus, the proposed method remarkably reduces the computation complexity by solving a small-scale problem. Theoretical analysis proves the efficiency of the proposed model. Furthermore, we extend LRR to a general model by using Schatten p-norm instead of nuclear norm and present a fast algorithm to solve large-scale problem. Experiments on MNIST and Caltech101 databse illustrate the equivalence of the proposed algorithm and the original LRR solver. Experimental results show that the proposed algorithm is remarkably faster than traditional LRR algorithm, especially in the case of large sample number.
Starting Page	1475
Ending Page	1480
Page Count	6
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 8, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6596
Journal	IET Image Processing
Publisher Date	2020-01-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Caltech101 Databse Computation Complexity Computational Complexity Data Handling Technique Large-scale Problem Large-scale SubSpace Clustering Low-rank Representation Matrix Algebra MNIST Original LRR Solver Pattern Clustering Singular Value Decomposition Small-scale Problem SubSpace Clustering Problem Traditional LRR Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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