Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search

Quantization based Fast Inner Product Search

We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, learned directly by minimizing the inner product quantization error. Then, the inner product of a query to a database vector is approximated as the sum of inner products with the subspace quantizers. Different from rec...

Evaluation of Product Quantization for Image Search

Product quantization is an effective quantization scheme, with that a high-dimensional space is decomposed into a Cartesian product of lowdimensional subspaces, and quantization in different subspaces is conducted separately. We briefly discuss the factors for designing a product quantizer, and then design experiments to comprehensively investigate how these factors influence performance of ima...

Vector Quantization for Image Compression-Concept Quantization

Improving Product Quantization through Space Partitioning

Product quantization [1] is a popular technique for compactly encoding high dimensional vectors to facilitate scalable approximate nearest neighbor (ANN) search on massive datasets. In this paper, we present a general extension to product quantization and its recent variants [2, 3]. Rather than learning a single global product quantizer in the original ambient space, our method partitions the s...

Robust Iterative Quantization for Efficient ℓp-norm Similarity Search

Iterative Quantization (ITQ) is one of the most successful hashing based nearest-neighbor search methods for large-scale information retrieval in the past a few years due to its simplicity and superior performance. However, the performance of this algorithm degrades significantly when dealing with noisy data. Additionally, it can barely facilitate a wide range of applications as the distortion ...