Abstract
We describe an effective approach to object or feature detection in point patterns via noise modeling. This is based on use of a redundant or non-pyramidal wavelet transform. Noise modeling is based on a Poisson process. We illustrate this new method with a range of examples. We use the close relationship between image (pixelated) and point representations to achieve the result of a clustering method with constant-time computational cost. We then proceed to generalize this method for high-dimensional data. Using a dataset of very well-known structure as a test case, we show proof of concept for this approach to analysis of high-dimensional boolean hyperlink datasets.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Murtagh, F., Starck, J.L., Berry, M. (1998). Clustering Based on Wavelet Transform: Applications to Point Pattern Clustering and to High-Dimensional Data Analysis. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_8
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DOI: https://doi.org/10.1007/978-3-642-72253-0_8
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