.png){kind=icon}
.png){kind=icon}
Return
Spherical random projection
.png){kind=icon}
PRE
AI
DOI：10.1093/jrsssb/qkae035.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
cluster validation
dimension reduction
random projection
spherical data
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W