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

Xiaojie Li; Jian Cheng Lv; Lili Li

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.

MACHINE learning; MACHINE theory; COMPUTATIONAL learning theory; COGNITIVE structures; DATA mining

Electronics Letters (Wiley-Blackwell), 2014, Vol 50, Issue 25, p1927

0013-5194

Academic Journal

10.1049/el.2014.2713