  Nearest neighbor search and k-nearest neighbor graph construction are two
fundamental issues arise from many disciplines such as multimedia information
retrieval, data-mining and machine learning. They become more and more imminent
given the big data emerge in various fields in recent years. In this paper, a
simple but effective solution both for approximate k-nearest neighbor search
and approximate k-nearest neighbor graph construction is presented. These two
issues are addressed jointly in our solution. On the one hand, the approximate
k-nearest neighbor graph construction is treated as a search task. Each sample
along with its k-nearest neighbors are joined into the k-nearest neighbor graph
by performing the nearest neighbor search sequentially on the graph under
construction. On the other hand, the built k-nearest neighbor graph is used to
support k-nearest neighbor search. Since the graph is built online, the dynamic
update on the graph, which is not possible from most of the existing solutions,
is supported. This solution is feasible for various distance measures. Its
effectiveness both as k-nearest neighbor construction and k-nearest neighbor
search approaches is verified across different types of data in different
scales, various dimensions and under different metrics.
