no code implementations • NeurIPS 2011 • Xiaoyin Ge, Issam I. Safa, Mikhail Belkin, Yusu Wang
While such data is often high-dimensional, it is of interest to approximate it with a low-dimensional or even one-dimensional space, since many important aspects of data are often intrinsically low-dimensional.
