Competitive Quantization for Approximate Nearest Neighbor Search
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|Publication status||Published - 2016|
|Publication type||A1 Journal article-refereed|
In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.