Johnson and Lindenstrauss (1984) proved that any finite set of data in a high dimensional space can be projected into a low dimensional space with the Euclidean metric information of the set being preserved within any desired accuracy. Such dimension reduction plays a critical role in many applications with massive data. There have been extensive effort in the literature on how to find explicit constructions of Johnson-Lindenstrauss projections. In this poster, we show how algebraic codes over finite fields can be used for fast Johnson-Lindenstrauss projections of data in high dimensional Euclidean spaces. This is joint work with Shuhong Gao and Yue Mao.
Gao, Shuhong; Knoll, Fiona; and Mao, Yue, "Johnson-Lindenstrauss projection of high dimensional data" (2015). Graduate Research and Discovery Symposium (GRADS). 122.