Abstract
In this paper, we propose an accelerated spectral clustering method, using a landmark selection strategy. According to the weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. The selected landmarks are provided to a landmark spectral clustering technique to achieve scalable and accurate clustering. In our experiments with two benchmark face and shape image data sets, we examine several landmark selection strategies for scalable spectral clustering that either ignore or consider the topological properties of the data in the affinity graph. Finally, we show that the proposed method outperforms baseline and accelerated spectral clustering methods, in terms of computational cost and clustering accuracy, respectively.
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Rafailidis, D., Constantinou, E., Manolopoulos, Y. (2014). Scalable Spectral Clustering with Weighted PageRank. In: Ait Ameur, Y., Bellatreche, L., Papadopoulos, G.A. (eds) Model and Data Engineering. MEDI 2014. Lecture Notes in Computer Science, vol 8748. Springer, Cham. https://doi.org/10.1007/978-3-319-11587-0_27
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DOI: https://doi.org/10.1007/978-3-319-11587-0_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11586-3
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