Abstract
Spectral clustering techniques have shown their capability to identify the data relationships using graph analysis, achieving better accuracy than traditional algorithms as k-means. Here, we propose a methodology to build automatically a graph representation over the input data for spectral clustering based approaches by taking into account the local and global sample structure. Regarding this, both the Euclidean and the geodesic distances are used to identify the main relationships between a given point and neighboring samples around it. Then, given the information about the local data structure, we estimate an affinity matrix by means of Gaussian kernel. Synthetic and real-world datasets are tested. Attained results show how our approach outperforms, in most of the cases, benchmark methods.
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Castro-Ospina, A.E., Álvarez-Meza, A.M., Castellanos-Domínguez, C.G. (2013). Automatic Graph Building Approach for Spectral Clustering. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_24
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DOI: https://doi.org/10.1007/978-3-642-41822-8_24
Publisher Name: Springer, Berlin, Heidelberg
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