18 June 2014 Geometric multi-resolution analysis based classification for high dimensional data
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Data sets are often modeled as point clouds lying in a high dimensional space. In practice, they usually reside on or near a much lower dimensional manifold embedded in the ambient space; this feature allows for both a simple representation of the data as well as accurate performance for statistical inference procedures such as estimation, regression and classification. In this paper we propose a framework based on geometric multi-resolution analysis (GMRA) to tackle the problem of classifying data lying around a low-dimensional set M embedded in a high-dimensional space RD. We test our algorithms on real data sets and demonstrate its efficacy in the presence of noise.
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Dung N. Tran, Dung N. Tran, Sang Peter Chin, Sang Peter Chin, "Geometric multi-resolution analysis based classification for high dimensional data", Proc. SPIE 9097, Cyber Sensing 2014, 90970L (18 June 2014); doi: 10.1117/12.2063316; https://doi.org/10.1117/12.2063316

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