Abstract
We present a novel scale adaptive, nonparametric approach to clustering point patterns. Clusters are detected by moving all points to their cluster cores using shift vectors. First, we propose a novel scale selection criterion based on local density isotropy which determines the neighborhoods over which the shift vectors are computed. We then construct a directed graph induced by these shift vectors. Clustering is obtained by simulating random walks on this digraph. We also examine the spectral properties of a similarity matrix obtained from the directed graph to obtain a K-way partitioning of the data. Additionally, we use the eigenvector alignment algorithm of [1] to automatically determine the number of clusters in the dataset. We also compare our approach with supervised[2] and completely unsupervised spectral clustering[1], normalized cuts[3], K-Means, and adaptive bandwidth meanshift[4] on MNIST digits, USPS digits and UCI machine learning data.
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Shetty, S., Ahuja, N. (2010). Supervised and Unsupervised Clustering with Probabilistic Shift. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15555-0_47
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DOI: https://doi.org/10.1007/978-3-642-15555-0_47
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