  In this paper we propose the creation of generic LSH families for the angular
distance based on Johnson-Lindenstrauss projections. We show that feature
hashing is a valid J-L projection and propose two new LSH families based on
feature hashing. These new LSH families are tested on both synthetic and real
datasets with very good results and a considerable performance improvement over
other LSH families. While the theoretical analysis is done for the angular
distance, these families can also be used in practice for the euclidean
distance with excellent results [2]. Our tests using real datasets show that
the proposed LSH functions work well for the euclidean distance.
