1. Online clustering via energy scoring based on low-rank and sparse representation.
- Author
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Xiaojie Li, Jian Cheng Lv, and Lili Li
- Subjects
- *
MACHINE learning , *MACHINE theory , *COMPUTATIONAL learning theory , *COGNITIVE structures , *DATA mining - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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