Summary
We discuss the use of orthogonal wavelet transforms in multivariate data analysis methods such as clustering and dimensionality reduction. Wavelet transforms allow us to introduce multiresolution approximation, and multiscale nonparametric regression or smoothing, in a natural and integrated way into the data analysis. Applications illustrate the powerfulness of this new perspective on data analysis.
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© 1998 Springer Japan
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Murtagh, F., Aussem, A. (1998). Using the Wavelet Transform for Multivariate Data Analysis and Time Series Analysis. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_67
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DOI: https://doi.org/10.1007/978-4-431-65950-1_67
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