Scalable Graph Hashing with Feature Transformation
Authors: Qing-Yuan Jiang, Wu-Jun Li
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two datasets with one million data points show that our SGH method can outperform the state-of-the-art methods in terms of both accuracy and scalability. |
| Researcher Affiliation | Academia | Qing-Yuan Jiang and Wu-Jun Li National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology, Nanjing University, China jiangqy@lamda.nju.edu.cn, liwujun@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Sequential learning algorithm for SGH |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We evaluate our method on two widely used large-scale benchmark datasets: TINY-1M [Liu et al., 2014] and MIRFLICKR-1M [Huiskes et al., 2010]. |
| Dataset Splits | No | For each dataset, we randomly select 5000 data points to construct the test (query) set and the remaining points will be used for training. No explicit mention of a separate validation set or validation split percentages/counts was found. |
| Hardware Specification | Yes | All the experiments are conducted on a workstation with Intel (R) CPU E5-2620V2@2.1G 12 cores and 64G RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their respective versions) that were used in the experiments. |
| Experiment Setup | Yes | For kernel feature construction, we use Gaussian kernel and take 300 randomly sampled points as kernel bases for our method. We set the parameter ρ = 2 in P(X) and Q(X). Here, γ is a very small positive number to avoid numerical problems, which is 10 6 in our experiments. |