A Local Smoothing and Geodesic Distance Based Clustering Algorithm for High Dimensional Noisy Data; Utilizing Embedded Geometric Structures
نویسنده
چکیده
A method that utilizes embedded geometric structure is proposed, to enhance clustering. The geometric structure is identified via a combination of a local smoothing method – local linear projection (LLP) – and a computational method for the geodesic distance. It is found that comparing to existing algorithms, it is more efficient to deal with noisy and “structured” data. Simulations for both synthetic data and microarray data are reported. We list some ideas for future improvement.
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تاریخ انتشار 2003