Regularized l1-Graph for Data Clustering
In Proceedings British Machine Vision Conference 2014
Abstractl1-Graph has been proven to be effective in data clustering, which partitions the data space by using the sparse representation of the data as the similarity measure. However, the sparse representation is performed for each datum independently without taking into account the geometric structure of the data. Motivated by l1-Graph and manifold leaning, we propose Regularized l1-Graph (Rl1-Graph) for data clustering. Compared to l1-Graph, the sparse representations of Rl1-Graph are regularized by the geometric information of the data. In accordance with the manifold assumption, the sparse representations vary smoothly along the geodesics of the data manifold through the similarity matrix constructed by the sparse codes. Experimental results on various data sets demonstrate the superiority of our algorithm compared to l1-Graph and other competing clustering methods.
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