The PQ implementation was tested on datasets ranging from 1,000 to 1,000,000 vectors (128 dimensions each), using 16 subspaces and 256 centroids per subspace. Key findings: Memory Efficiency: ...

A variety of modifications have been employed to learning vector quantization (LVQ) algorithms using either crisp or soft windows for selection of data. Although these schemes have been shown in ...

Abstract: 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 ...

Product quantization (PQ) is an effective vector quantization method. A product quantizer can generate an exponentially large codebook at very low memory/time cost. The essence of PQ is to decompose ...