NDLI: Online clustering via energy scoring based on low-rank and sparse representation

Content Provider	IET Digital Library
Author	Li, Xiaojie Lv, Jian Cheng Li, Lili
Abstract	Subspace clustering is very useful in many fields, such as computer vision and machine learning. However, most of the clustering methods cannot deal with out-of-sample data directly. For each new sample, these methods need to relearn the representations of all (new and original) data for clustering. This is unrealistic in many practical applications. A new online clustering method to cluster out-of-sample data in terms of the meaningful energy scores of data is proposed. By interpreting low-rank representation (LRR) as a dynamical system, a computation method for energy scores of data has been developed. The scores can be calculated by integration, independent of the LRR learning procedure. Then, a linear classifier is used to cluster out-of-sample data using their energy scores. Experimental results demonstrate the effectiveness and efficiency of the method.
Starting Page	1927
Ending Page	1929
Page Count	3
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2014.2713
Journal	Electronics Letters
Publisher Date	2014-12-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Data Energy Scores Data Handling Technique Dynamical System Energy Scoring Knowledge Engineering Technique Learning in AI Linear Classifier Low-rank Representation LRR Learning Procedure Online Clustering Method Out-of-sample Data Clustering Pattern Classification Pattern Clustering Sparse Representation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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