Open AccessOnline clustering via energy scoring based on low‐rank and sparse representation
Author(s) -
Li Xiaojie,
Lv Jian Cheng,
Li Lili
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.2713
Subject(s) - cluster analysis , computer science , artificial intelligence , sample (material) , data mining , rank (graph theory) , pattern recognition (psychology) , computation , machine learning , classifier (uml) , subspace topology , energy (signal processing) , representation (politics) , clustering high dimensional data , external data representation , mathematics , statistics , algorithm , chemistry , chromatography , combinatorics , politics , political science , law
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.