  Nearest neighbor search is a very active field in machine learning for it
appears in many application cases, including classification and object
retrieval. In its canonical version, the complexity of the search is linear
with both the dimension and the cardinal of the collection of vectors the
search is performed in. Recently many works have focused on reducing the
dimension of vectors using quantization techniques or hashing, while providing
an approximate result. In this paper we focus instead on tackling the cardinal
of the collection of vectors. Namely, we introduce a technique that partitions
the collection of vectors and stores each part in its own associative memory.
When a query vector is given to the system, associative memories are polled to
identify which one contain the closest match. Then an exhaustive search is
conducted only on the part of vectors stored in the selected associative
memory. We study the effectiveness of the system when messages to store are
generated from i.i.d. uniform $\pm$1 random variables or 0-1 sparse i.i.d.
random variables. We also conduct experiment on both synthetic data and real
data and show it is possible to achieve interesting trade-offs between
complexity and accuracy.

Machine Learning;Probability