  Vector quantization-based approaches are successful to solve Approximate
Nearest Neighbor (ANN) problems which are critical to many applications. The
idea is to generate effective encodings to allow fast distance approximation.
We propose quantization-based methods should partition the data space finely
and exhibit locality of the dataset to allow efficient non-exhaustive search.
In this paper, we introduce the concept of High Capacity Locality Aggregating
Encodings (HCLAE) to this end, and propose Dictionary Annealing (DA) to learn
HCLAE by a simulated annealing procedure. The quantization error is lower than
other state-of-the-art. The algorithms of DA can be easily extended to an
online learning scheme, allowing effective handle of large scale data. Further,
we propose Aggregating-Tree (A-Tree), a non-exhaustive search method using
HCLAE to perform efficient ANN-Search. A-Tree achieves magnitudes of speed-up
on ANN-Search tasks, compared to the state-of-the-art.
