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Spherical random projection
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DOI：10.1093/jrsssb/qkae035.png)
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We propose a new method for dimension reduction of high-dimensional spherical data based on the nonlinear projection of sphere-valued data to a randomly chosen subsphere. The proposed method, spherical random projection, leads to a probabilistic lower-dimensional mapping of spherical data into a subsphere of the original. In this paper, we investigate some properties of spherical random projection, including expectation preservation and distance concentration, from which we derive an analogue of the Johnson-Lindenstrauss Lemma for spherical random projection. Clustering model selection is discussed as an application of spherical random projection, and numerical experiments are conducted using real and simulated data.
Keyword:
cluster validation
dimension reduction
random projection
spherical data
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
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