Abstract
Clustering is a fundamental tool that has been applied in dealing with huge volumes of text documents and images. For extracting relevant information from the enormous volumes of available data, some co-clustering algorithms have been proposed and shown to be superior to traditional one-side clustering. In this paper, we proposed a novel co-clustering approach called double sparse manifold learning (DSML). We based our formulation on double sparse constraints and manifold learning which use a modified version of mutual k-nearest neighbor graph to capture the underlying structure, modeled sample-feature relationship from the data reconstruction perspective. We developed an iterative procedure to get the solution. Our method preserves local geometrical structure better. Experiments on three benchmark datasets show that our method can get more promising performance on all analyzed data-sets.
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Acknowledgements
This research is supported by National Natural Science Foundation of China, under Grant No. 61272304. Supported by National Nature Science Foundation under Grant No. 61363029. Supported by The Key Laboratory of image and graphic intelligent processing of higher education in Guangxi (No. GIIP201606). Supported by Guangxi Key Laboratory of Trusted Software (No. kx201628)
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Li, F., Zhang, S. (2017). Co-clustering with Manifold and Double Sparse Representation. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_31
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DOI: https://doi.org/10.1007/978-3-319-68935-7_31
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