Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing
This work addresses a bottleneck in high-dimensional ANN search for applications like information retrieval, but it is incremental as it builds on existing vector quantization methods.
The paper tackled the problem of optimizing dictionaries for vector quantization in approximate nearest neighbor search by proposing Dictionary Annealing, which reduces quantization error and improves search performance compared to state-of-the-art methods, with experiments showing substantial error reduction.
We introduce a novel dictionary optimization method for high-dimensional vector quantization employed in approximate nearest neighbor (ANN) search. Vector quantization methods first seek a series of dictionaries, then approximate each vector by a sum of elements selected from these dictionaries. An optimal series of dictionaries should be mutually independent, and each dictionary should generate a balanced encoding for the target dataset. Existing methods did not explicitly consider this. To achieve these goals along with minimizing the quantization error (residue), we propose a novel dictionary optimization method called \emph{Dictionary Annealing} that alternatively "heats up" a single dictionary by generating an intermediate dataset with residual vectors, "cools down" the dictionary by fitting the intermediate dataset, then extracts the new residual vectors for the next iteration. Better codes can be learned by DA for the ANN search tasks. DA is easily implemented on GPU to utilize the latest computing technology, and can easily extended to an online dictionary learning scheme. We show by experiments that our optimized dictionaries substantially reduce the overall quantization error. Jointly used with residual vector quantization, our optimized dictionaries lead to a better approximate nearest neighbor search performance compared to the state-of-the-art methods.