Abstract
A number of nonlinear dimensionality reduction, or graph embedding, techniques have been proposed recently. These embedding techniques aim to provide a low-dimensional depiction of graphs while preserving certain properties of the data. In this manuscript we propose a novel graph embedding method which tries to optimize the “modularity” of graphs during dimensionality reduction. The embedding method has a simple formulation and is naturally relaxed and solved by a convex semi-definite program, with the guaranteed global optimum. We evaluate the performance of the method with a variety of examples and the method reports promising results in inspecting the cluster structures of graphs.
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Li, W. (2013). Modularity Embedding. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_12
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DOI: https://doi.org/10.1007/978-3-642-42042-9_12
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