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Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named residual vector...
We present a novel nearest neighbor search scheme named aggregating tree (A-Tree) for high dimensional data that uses vector quantization encodings (VQ-encodings) to build a radix tree, and perform the nearest neighbor search by beam search. To search accurately and efficiently, we suggest VQ-encodings to satisfy locally aggregating encoding criterion: for any node of the corresponding A-Tree, neighboring...
Vector quantization based methods are important tools for large scale search. They perform lossy compression on the dataset, then the distance between an uncompressed vector and compressed dataset can be efficiently computed via asymmetric distance computation (ADC). We show that for large datasets, ADC conducts excessive computations on vectors sharing the same prefix. To this end, we propose encoding...
Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named residual vector...
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