Fast k-Nearest Neighbour Search via Prioritized DCI
Authors: Ke Li, Jitendra Malik
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate empirically that Prioritized DCI significantly outperforms prior methods. In particular, relative to Locality-Sensitive Hashing (LSH), Prioritized DCI reduces the number of distance evaluations by a factor of 14 to 116 and the memory consumption by a factor of 21. |
| Researcher Affiliation | Academia | 1University of California, Berkeley, CA 94720, United States. Correspondence to: Ke Li <ke.li@eecs.berkeley.edu>. |
| Pseudocode | Yes | Algorithm 1 Data structure construction procedure |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | Evaluation is performed on two datasets, CIFAR100 (Krizhevsky & Hinton, 2009) and MNIST (Le Cun et al., 1998). |
| Dataset Splits | Yes | We evaluate performance of all algorithms using crossvalidation, where we randomly choose ten different splits of query vs. data points. Each split consists of 100 points from the dataset that serve as queries, with the remainder designated as data points. |
| Hardware Specification | No | The paper mentions 'wall-clock time' for experiments but does not provide any specific hardware details such as CPU/GPU models or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For LSH, we used 24 hashes per table and 100 tables... For product quantization, we used a data-independent codebook with 256 entries... For standard DCI, we used the same hyparameter settings used in (Li & Malik, 2016) (m = 25 and L = 2 on CIFAR-100 and m = 15 and L = 3 on MNIST). For Prioritized DCI, we used two different settings: one that matches the hyperparameter settings of standard DCI, and another that uses less space (m = 10 and L = 2 on both CIFAR-100 and MNIST). |